Does the Threat of Takeover Discipline Managers? New ...€¦ · 1 1.0 INTRODUCTION Theoretical...

Does the Threat of Takeover Discipline Managers? New Evidence from the Foreign Investment and National Security Act

June 2018

David Godsell Assistant Professor

University of Illinois at Urbana-Champaign College of Business

Wohlers Hall Suite 396 1206 South Sixth Street

Champaign, Illinois, 61820, USA Tel: 1.217.300.0844

E-mail: [email protected]

I acknowledge very helpful and constructive comments on the manuscript from Margaret Abernethy, Paul Avey, Vishal Baloria, Andy Bauer, Brooke Beyer, Erv Black, Matthew Boland, Paul Calluzzo, Yingwen Guo, Bowe Hansen, Jingjing Huang, Xue Jia, Scott Johnson, Melissa Lewis-Western, Jack Maher, Sattar Mansi, Steve Rogers, Stefan Schantl, Hollis Skaife, Chris Stewart, Joseph Weber, Mike Welker, Jin Xu, Ning Zhang, Jingwen Zhao, Mi Zhou workshop participants at the University of Melbourne, the University of Illinois at Urbana-Champaign and participants of the 2016 Brigham Young University Accounting Research Symposium, the 2018 Hawaii Accounting Research Conference and the 2018 AAA IAS Mid-Year Meeting.

Does the Threat of Takeover Discipline Managers? New Evidence from the Foreign Investment and National Security Act

ABSTRACT

Prior literature suggests a strong takeover market accentuates earnings management but these findings 1) are

drawn from confounded settings and 2) conflict with evidence that the takeover market is a managerial

disciplining mechanism. In this paper, I describe a credible exogenous shock to the takeover market, the Foreign

Investment and National Security Act (FINSA), which suppressed takeover activity in a subset of U.S. industries

comprising one-third of the Compustat universe. I exploit this exogenous decline in takeover activity to

investigate the effect of the takeover market on financial reporting quality using a difference-in-differences

(DiD) research design with firm and year fixed effects. I find FINSA-affected firms boost income through

accruals after FINSA, but record fewer unsigned discretionary accruals. Fewer income-decreasing discretionary

accruals with no change in income-increasing accruals drive this effect. The effect is economically large. The

reduction in income-decreasing discretionary accruals boosts net income by 8.5% on average, or by 0.42% of

total assets. Using a triple-DiD research design I show the effect varies predictably. Firms more (less) likely to

be taken over pre-FINSA should be more (less) affected by changes in the takeover market and I find that

firms more likely to be taken over pre-FINSA drive my main result. Using this triple-DiD specification, I further

find FINSA-affected firms record fewer income-decreasing special items and fewer write-downs after FINSA.

Relatedly, I find FINSA-affected firms exhibit lower financial reporting conservatism after FINSA. My

inferences are robust to several alternate discretionary accrual models, and specifications that address recent

concerns about discretionary accrual model confounds. Overall, I show that stronger takeover markets curb,

rather than accentuate, earnings management.

Keywords: FINSA, protectionist laws, economic nationalism, mergers and acquisitions laws, corporate

control, earnings quality

JEL Code: F52; M41; G34; G38; K22

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1.0 INTRODUCTION

Theoretical research indicates that an active market for corporate control (hereafter referred to as the

takeover market) disciplines directors and managers,1 with empirical evidence indicating a positive association

between the strength of the takeover market and CEO turnover.2 However, the prior accounting literature

suggests an active takeover market increases rather than decreases earnings management. These prior studies

examine sources of variation in takeover activity subject to serious empirical confounds (the adoption of firm-

level antitakeover provisions or state-level antitakeover laws) and so the effect of takeover activity on financial

reporting quality is an unresolved question.

Competing theories predict opposing outcomes. The managerial entrenchment hypothesis predicts that

managerial opportunism, absent the disciplining effects of an active takeover market, will lead to heightened

earnings management due to compensation incentives (Healy and Whalen, 1999; Fama and Jensen, 1983; Fama,

1980). Compensation incentives drive earnings management because compensation is linked to accounting

returns and market prices (Healy, 1985; Gaver et al., 1995; Holthausen et al., 1995). Other theories suggest a

weak takeover market may instead lead to less earnings management, because a weak takeover market induces

managers to 1) indulge in the “quiet life” and pursue low-risk projects, 2) adopt a longer horizon and make

decisions more aligned with those of investors, 3) make less effort to hide poor firm performance, or 4) increase

earnings quality to signal firm quality to debt and equity investors (Stein, 1988; Bertrand and Mullainathan,

2003).

The literature to date finds that managers record fewer discretionary accruals when the threat of takeover

declines (see, e.g., Zhao and Chen, 2008a, 2008b, 2009). Yet, these findings conflict with empirical evidence of

a positive association between entrenched managers and lower-quality financial reporting (Larcker, Richardson

and Tuna, 2007; Francis, Schipper and Vincent, 2005; Schleifer and Vishny, 1989; Hermalin and Weisbach,

2012). Injecting additional ambiguity into this area of research are recent papers criticizing the research settings

used to examine the relation between the takeover market and earnings management. Karpoff and Wittry

1 See, e.g., Jensen (1993); Scharfstein (1988); Manne (1965); Williamson (1983); Grossman and Hart (1980); Macey (1988); Hirshleifer and Thakor (1998). 2 See, e.g., Lel and Miller (2015); Harford (2003); Bebchuk, Fried and Walker (2002).

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(2017), among many others,3 list identification concerns inherent in research designs that use firm choice of

antitakeover provisions or staggered state antitakeover laws to identify exogenous shifts in the takeover market.

Hence, my objective in this study is to investigate the effect of the takeover market on managers’ accrual choices

using a new setting that, as I will demonstrate, is not susceptible to the endogeneity concerns characterizing

research designs used in the prior literature.

The Foreign Investment and National Security Act (FINSA) provides a powerful setting in which to

examine the effects of exogenous variation in the takeover market. FINSA added substantial costs to foreign

takeovers of U.S. firms in a subset of industries starting in 2009 by spurring an obscure and inactive regulatory

committee, the Committee on Foreign Investment in the United States (CFIUS), to adjudicate foreign

investment proposals. FINSA increased the costs of foreign takeover in four ways: first, by increasing the

likelihood of an extended CFIUS investigation of a proposed foreign takeover;4 second, by increasing U.S.

Congressional involvement in the regulatory approval process and, thereby, political uncertainty for foreign

acquirers;5 third, by increasing the number of national security-related concessions required from foreign

acquirers before takeover approval;6 and fourth, by increasing the enforcement of, and penalties related to

lapses in, commitments made by the foreign acquirer to mitigate ongoing national security concerns.7 If costs

associated with these frictions are large, then foreign investment in the U.S. after FINSA will decline and the

takeover market will weaken.

3 Atanasov and Black (2015); Lel and Miller (2015); Catan and Kahan (2016); Werner and Coleman (2015); and Armour and Skeel (2007) also list shortcomings of research designs that use firm choice of antitakeover provisions or staggered state antitakeover laws to identify exogenous shifts in the takeover market. 4 An investigation subsequent to the 30-day CFIUS review extends the CFIUS process by 45 days. Indicative of these significant changes to the Defense Production Act, the percentage of foreign investment notices investigated by the CFIUS skyrocketed starting in 2007, as shown in Figure 1. Lengthy approval periods are costly for foreign investors because they increase the probability of competing bids (Jarrell and Bradley, 1980). 5 Bhagwat, Dam and Harford (2016) provide evidence that uncertainty decreases merger and acquisition activity—e.g., “Morgan Stanley said its sale of certain oil-trading and storage businesses to OAO Rosneft may fall apart, as tensions between the U.S. and Russian governments leave the deal in limbo…. Confidence that the sale will ever secure CFIUS's blessing has faded as the U.S. escalated its response to Russia's interference in Ukraine” (Dow Jones News Service, October 10, 2014). 6 E.g., forcing the target to forfeit sales to the U.S. government or to comply with requests to open books and facilities to authorities without warrant (Byrne, 2015: 877). 7 E.g., a penalty up to the value of the transaction for mitigation agreement breaches.

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Anecdotal evidence suggests costs imposed by FINSA upon foreign acquirers are substantial. Figure 1

illustrates the striking increase in CFIUS investigations from less than 1% of proposed foreign investments pre-

FINSA to more than 40% post-FINSA. Figure 2 shows there were fewer than 400 news articles mentioning

the CFIUS between 1989 and 2005 while there have been more than 10,000 mentions since 2005. Articles

mentioning both “CFIUS” and “withdraw” (as in, “withdrawn offer”) also indicate the frictions added by

FINSA. Between 1989 and 2005, these articles numbered in the dozens while, since 2005, there have been more

than 350 articles including both terms. Figure 3 captures the importance of the CFIUS to public firms by

showing the number of firms that mention either “CFIUS” or the “Defense Production Act” in SEC filings.

Prior to 2006, there were very few mentions in 10-K filings with fewer than 50 firms in any year referring to

the CFIUS or its guiding legislation. Mentions of the CFIUS in 10-K filings after FINSA increased significantly

with more than 250 mentions in 2007. Finally, all recorded mentions of the CFIUS during U.S. earnings

conference calls occur in and after 2006.8

These observations suggest FINSA was a strong shock to the takeover market. The delivery of a strong

shock to the takeover market is an important prerequisite to using FINSA as an instrument to examine variation

in takeover activity. Karpoff and Wittry (2017) and Cain, Mckeon and Solomon (2017), for example, criticize

the state-antitakeover law literature because state antitakeover laws appear to have no discernible effect on

takeover activity. In contrast, and demonstrating the credibility of FINSA as an exogenous source of variation

in takeover activity, Godsell, Lel and Miller (2017) provide empirical evidence that FINSA had a substantial

moderating effect (no effect) on the frequency and magnitude of foreign (domestic) takeovers. Relative to a

control group of firms unaffected by FINSA, they find a 74% decline in foreign merger and acquisition (M&A)

activity for FINSA-affected firms after FINSA. Contemporaneous changes in domestic takeovers are

indistinguishable from zero.

I use the exogenous variation in the takeover market provided by FINSA to examine the effect of the

takeover market on managers’ accrual choices. An important feature of my setting is that the CFIUS is an

opaque regulatory committee. Unlike the U.S. Department of Justice, the U.S. International Trade Commission

8 Earnings call data drawn from www.seekingalpha.com. Data retrieved October 7, 2016.

http://www.seekingalpha.com/

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and the U.S. Department of Commerce, it does not issue public reports detailing the nature of its activities or

the firms with which it interacts. This inhibits detailed firm-level analyses. Furthermore, the CFIUS is

impervious to Freedom of Information Act (FOIA) requests that would pierce the veil of CFIUS secrecy

because, as part of the executive branch of the U.S. government, presidential privilege renders CFIUS immune

to FOIA requests. To draw insights into the behavior of FINSA-affected firms, I rely on a hand-collected set

of firms that have interacted with CFIUS. I hand-collect all press articles and SEC filings that mention the

CFIUS or FINSA. In piercing the veil of secrecy around CFIUS activities, I identify over 600 firms that were

subject to CFIUS scrutiny before and after FINSA. I define my treatment group (FINSA-affected firms) using

the 4-digit SIC codes drawn from the firms in my hand-collected sample.

I employ accrual-based measures of earnings management to identify managers’ response to an attenuated

takeover market. I find evidence in support of managerial entrenchment theory, and inconsistent with theories

predicting decreased earnings management. Using a DiD research design with firm and year fixed effects, I find

economically and statistically significant increases in income boosting discretionary accruals. My main result

suggests that FINSA-affected firms record more income boosting discretionary accruals after FINSA, relative

to control firms, equal to 0.42% (0.43%) of total assets, or approximately 8.5% (8.9%) of average ROA. This

result provides evidence that a weakened takeover market leads to higher rather than lower levels of earnings

management.

However, in subsequent analysis, I find FINSA-affected firms record fewer unsigned discretionary accruals

after FINSA. I re-estimate my accruals model after decomposing discretionary accruals into the absolute value

of positive and negative discretionary accruals to address the paradox of more income-boosting accruals but

fewer discretionary accruals. I find that decreases in income-decreasing discretionary accruals recorded by

FINSA-affected firms drive my main result. Fewer income-decreasing discretionary accruals is consistent with

more income boosting discretionary accruals and with fewer discretionary accruals.

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Using a triple-DiD research design, I further find that firms more likely to be taken over pre-FINSA drive

my main result.9 I observe no change in the discretionary accruals reported by FINSA-affected firms with

below-median likelihood of takeover. This is an intuitive result because changes in the takeover market should

have a greater effect on firms that, before FINSA, were more likely to be subject to takeover.

I generate my main measure of discretionary accruals using the the residual from the performance-adjusted

(Kothari, Leone and Wasley, 2005) modified-Jones (1991) model (Dechow, Sloan and Sweeney, 1995) accrual

model. Accrual-based measures of earnings management rely on models attempting to capture normal accruals,

and some measures are potentially subject to biased coefficients and biased standard errors (Chen, Hribar and

Melessa, 2018). To address concerns that discretionary accrual models spuriously generate my findings I present

my main results using both one-step and two-step discretionary accrual models and find very similar results

between models. I also examine special items and write-downs to address concerns regarding discretionary

accrual models. Consistent with the foregoing, I find that FINSA-affected firms record more income-boosting

special items and that a reduction in income-decreasing special items drives this effect. I further find that

FINSA-affected firms record fewer write-downs after FINSA relative to a control group of firms unaffected

by FINSA. These findings support the notion that a weakened takeover market increases earnings management.

I infer that fewer income-decreasing accruals suggests that the pace at which FINSA-affected firms

recognize losses slows relative to control firms. To corroborate my main finding, I estimate the Basu (1997)

earnings-return model to examine whether variation in the takeover market affects financial reporting

conservatism. Consistent with fewer income-decreasing accruals reported in my main tests, I find that FINSA-

affected firms exhibit significantly less financial reporting conservatism after FINSA, relative to control firms.

My results are robust to several alternative discretionary accrual models. My results are robust to using

instead the residual from the original Jones (1991) model; the modified Jones model (Dechow, Sloan and

Sweeney, 1995); the modified Jones model including lead, lagged and contemporaneous cash flows (Dechow

and Dichev, 2002; McNichols, 2002); the Jones (1991) with controls for economics gains and losses (Ball and

9 Pre-FINSA firm-year takeover probabilities are calculated by estimating a takeover prediction model developed by Cremers, Nair and John (2009) and used in Armstrong, Balakrishnan and Cohen (2012), Karpoff et al. (2017) and Godsell, Lel and Miller (2017).

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Shivakumar (2006); performance-adjusted (Kothari et al., 2005) modified Jones (1991) model (Dechow, Sloan

and Sweeney, 1995) with firm and year fixed effects (Kothari, Mizik and Roychowdhury, 2016), and a model

including all parameters listed in the foregoing models.

My results are also robust to using conservative empirical specifications appropriate for my setting. For

example, I estimate all models with firm and year fixed effects due to the substantial heterogeneity in the accrual

generating process between firms (Owens, Wu and Zimmerman, 2017). Including these fixed effects guards

against spurious results caused by heterogeneous variation across firms and time (Gormley and Matsa, 2014).

Several robustness tests further validate inferences drawn from my main results. First, because my results

are strongest for treatment firms more like to be taken over prior to FINSA, a concern is that accruals are

correlated with takeover probability. To address this concern I include takeover determinants in all estimations.

Second, a concern is that accrual models cannot differentiate between genuine economic activity and accruals

resulting from managerial opportunism. To address this concern, in addition to using firm fixed effects, I

include additional control variables capturing variation in economic activity, including measures capturing firm

losses, operating cycle, stock volatility, the standard deviation of earnings and cash flows, and measures

capturing economic gains and losses (Ball and Shivakumar, 2006; Francis et al., 2004). Third, I address concerns

that FINSA legislation was unduly influenced by private interests. My exogeneity assumption is violated if U.S.

firms influenced FINSA legislation. An examination of lobbying records made available by the Secretary of the

Senate’s Office of Public Records shows that, of the 31 companies that lobbied for or against FINSA, 13 were

U.S. companies.10 Of these 13 companies, five were firms directly involved in facilitating M&A transactions

and likely to be motivated by business concerns. Nonetheless, because it is possible that lobbying firms will

influence legislative outcomes, I exclude lobbying firms from my analysis and am able to replicate my results.

Finally, my results are robust to dropping financial recession years and to placebo tests that randomize the year

and industry of treatment.

10 Of the 13 companies, several were firms directly involved in the M&A business with no apparent vested interest in protecting managerial entrenchment. The lobbying firms were Boeing Company, Carlyle Group, Conoco Philips, EDS Corporation, Exxon Mobil, General Electric, Goldman & Sachs, Halliburton, JP Morgan Chase, Lehman Brothers, Merrill Lynch, United Technologies Corporations and Xcel Energy.

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My findings are relevant not only to U.S. legislators interested in the consequences of regulating foreign

investment but also to legislators in the increasing number of countries that are considering or have recently

passed FINSA-like legislation (e.g., China, France and India [Steinitz and Ingrassia, 2009]). From a policy and

social welfare perspective, it is also important to understand the economic consequences of periodically

recurring financial protectionism and nativism.11 My results show that financial protectionism has important

financial reporting consequences for investors, analysts, auditors, board directors and regulators.

The rest of this paper is as follows. I describe the prior literature, the CFIUS and FINSA in Section 2. I

then develop my hypotheses. In Section 3, I describe my sample and research design. Section 4 presents my

empirical results, and Section 5 concludes.

2.0 INSTITUTIONAL BACKGROUND AND HYPOTHESIS DEVELOPMENT

2.1 Theoretical links between takeover activity and earnings management

The literature examining the effect of the takeover market on earnings management (Armstrong,

Balakrishnan and Cohen, 2012; Zhao and Chen, 2008a, 2008b, 2009) advances five theories. Armstrong et al.

(2012) advance the managerial entrenchment, signalling, career concerns and quiet life theories and find support

for the signalling theory. Three studies by Zhao and Chen (2008a, 2008b, 2009) advance the entrenchment,

alignment and quiet life theories and find evidence supporting the quiet life theory.

Management entrenchment theory predicts that earnings quality will decline after a negative shock to the

takeover market because the takeover market is a managerial monitoring mechanism (Manne, 1965). An

attenuated takeover market provides more leeway for managers to extract rents from the firm through, for

example, increasing earnings-based compensation by reporting income-increasing discretionary accruals. Liu

and Lu (2007) find that firms use accruals to manage earnings upward to tunnel resources out of the firm. These

diversions attract less scrutiny when the firm is reporting stable profits (Kim and Yi, 2006; Harris, Hobson and

11 For example, public sentiment leading to the targeting foreign investment appears cyclical and subject to ebbs and flows. A legislative history offers some insight: the Pickett Act of 1909 enacted limits to foreign claims on western U.S. oil-producing land, Congress authorized the President to restrict foreign investment in the fledgling radio industry in 1912, the Mineral Lands Leasing Act of 1920 limited foreign oil company’s ability to drill in the U.S., the Defense Production Act of 1950 gave the U.S. President the power to reject foreign investments under a state of emergency, the Exon-Florio Amendment of 1988 to the Defense Production Act liberalized the terms under which the U.S. President could reject foreign investment, and the Byrd Amendment of 1993 required rather than permitted the U.S. President to investigate foreign investments when made by a foreign government-controlled entity.

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Jackson, 2016). In contrast, the alignment theory predicts that an attenuated threat of takeover extends

managers’ time horizon to match investors’. Overlapping horizons induce managers to act in shareholders’ best

interest and record fewer discretionary accruals. The quiet life theory predicts that a weakened takeover market

will facilitate lower-risk but suboptimal investment selection, which, in turn, leads to fewer income-increasing

discretionary accruals recorded in anticipation of uncertain future cash flows and, ultimately, higher earnings

quality. Zhao and Chen (2008a, 2008b) find that firm-level antitakeover defenses are associated with fewer

discretionary accruals. In a study examining the state antitakeover law setting, Zhao and Chen (2009) find that

firms subject to state-level antitakeover laws have lower discretionary total accruals. The three highly related

studies authored by Zhao and Chen find support for the quiet life theory.

Endogeneity in this prior literature motivates Armstrong et al. (2012) to re-examine this setting.12 They find

similar results but offer a different interpretation. They posit that managerial career concerns and managerial

signalling could explain higher earnings quality following state-level antitakeover laws. The career concerns

theory predicts that managers face fewer career risks when the takeover market is tempered; managers will

record fewer income-increasing discretionary accruals with the overall effect of increasing earnings quality when

career pressures are weakened. The signalling theory predicts that market participants are cognizant of the

managerial disciplining role played by the takeover market and will take steps to protect themselves if the

takeover market is weakened. This theory predicts that managers keen to access debt and equity markets will

pre-empt this investor response by improving the quality of financial reporting.

Armstrong et al. (2012) provide evidence that earnings informativeness improves subsequent to the

staggered introduction of state antitakeover laws and that this improvement is concentrated in firms for which

it is advantageous to signal their quality prior to accessing equity markets. Ultimately, Armstrong et al. (2012)

find an increase in earnings quality following the staggered adoption of state antitakeover laws. Of the four

foregoing theories predicting fewer discretionary accruals (quiet life, career concerns and signalling), they find

support for signalling theory.

12 They write, “By using the passage of the state antitakeover laws as an exogenous increase in firms’ protection from hostile takeovers, our results are less susceptible to endogeneity concerns that plague the existing literature” (p. 187).

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To the best of my knowledge, no studies examining changes in firm-level antitakeover provisions or state-

level antitakeover laws find evidence of subsequent income-increasing accrual choices that would support the

managerial entrenchment hypothesis. However, the shortcomings of previously examined settings, described

next, warrant re-examination of the nature of this relationship. First, Lel and Miller (2015) note that managers

have significant discretion in choosing takeover defenses. Atanasov and Black (2015) provide commentary on

the many issues facing a research design that addresses endogeneity only through the inclusion of additional

variables in a multivariate regression.13 Second, difference-in-difference research designs using the adoption of

state antitakeover laws are now subject to a long list of criticisms. Each study examining this setting argues that

changes in antitakeover laws are exogenous to firm choices because this claim is made by Bertrand and

Mullainathan (2003). However, the claim that state antitakeover laws exogenously vary the takeover market is

now subject to skepticism.14 Other criticisms are abundant.

Catan and Kahan (2016) and Klausner (2013) call for abandoning the antitakeover literature because

manager and director discretion in adopting poison pills dominates the effects of state antitakeover legislation.

Catan and Kahan also find that results in three antitakeover studies (Cheng, Nagar and Rajan, 2005; Garvey

and Hanka, 1999; Qiu and Yu, 2009) are sensitive to modest changes in empirical specification (e.g., inclusion

of state of incorporation, year or firm fixed effects) or to controls for contemporaneous events. Karpoff and

Wittry (2017) temper the call to abandon the state antitakeover setting but, nonetheless, add additional

criticisms. Karpoff and Wittry find that the prior literature is sensitive to controlling for 1) other state

antitakeover laws; 2) pre-existing firm-level takeover defenses; 3) unexamined court decisions affecting state

antitakeover laws; and 4) evidence that in-state firms i) lobby for state antitakeover laws (Werner and Coleman,

2015; Gartman, 2000; Karpoff and Malatesta, 1989), ii) can relocate to states with preferred antitakeover laws

and iii) can use opt-out provisions embedded in some state antitakeover laws.15 Cain et al. (2017) also note a

13 Zhao and Chen (2008a, p. 1349) accordingly caution, “…although we have tried to address the problem of endogeneity of staggered boards through the inclusion of several control variables in the multivariate analysis, we cannot include all the factors.” 14 See Karpoff and Wittry (2017) for a full discussion of the fallibility of this claim. 15 Karpoff and Wittry (2017) find that 10.75% of firms in the ISS Governance database chose to opt out of at least one state antitakeover law for at least one year between 1990 and 2012. Wald and Long (2007) find that firms are more likely to incorporate in-state when states have a greater number of antitakeover provisions.

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major shortcoming. In an examination of 65 years of takeover laws, Cain et al. find no association between

state antitakeover laws and variation in takeovers and the takeover market.16

Zhao and Chen (2009) and Armstrong et al. (2012) use the state antitakeover law setting to draw their

inferences regarding the relation between the takeover market and earnings management. The shortcomings of

this setting weaken the reliability of inferences drawn. Adversity in drawing conclusive inferences in the state

antitakeover setting necessitates a re-examination of the relationship between the takeover market and earnings

management in settings that offer more plausibly exogenous variation in the takeover market.

In contrast with previously examined settings, my inferences are reliable because the research design I use

allows me to address several empirical limitations of the firm antitakeover provision setting and the staggered

adoption of state antitakeover law setting. First, FINSA affected a subset of industries comprising

approximately one-third of my sample, allowing for cross-sectional analysis vis-à-vis the state antitakeover laws,

which affected 0% of firms in 1985 but 95% of firms by 1990 (Cain et al., 2017). Using DiD (triple-DiD)

research designs, I can identify appropriate control groups unaffected (or less affected within the treated group)

by FINSA and draw more credible inferences regarding the effect of the takeover market on earnings

management. Second, federal enactment of FINSA, with very few domestic firms lobbying for or against the

Act, addresses the endogeneity concerns in the state antitakeover law setting where firms have more influence

in legislative outcomes (see, e.g., Catan and Kahan, 2016; Karpoff and Wittry, 2017; Werner and Coleman,

2015). Third, the change I examine in the takeover market is not under the influence of management (i.e.,

treatment by regulators of a potential foreign takeover is the same whether management welcomes the proposed

foreign takeover or not).17 Fourth, dissimilar from state-level antitakeover laws, FINSA-affected firms cannot

choose to shop for legislation by shifting incorporation to jurisdictions where FINSA does not apply. The

16 In prior studies of the effect of state antitakeover laws on the takeover market, Comment and Schwert (1995) find no change in M&A activity subsequent to takeover laws while later analysis by Schwert (2000) finds a decline over a longer period. However, as mentioned, Cain et al. (2017) provide the most complete analysis. 17 An overlooked but important feature of the antitakeover literature is that antitakeover laws are inconsequential when management and directors welcome the takeover. This is important because hostile deals are much less frequent than friendly deals. The prior literature reports that, depending on the period, hostile deals comprise only 30%, 4%, or 1–5% of all takeovers (Schwert, 2000; Andrade, Mitchell and Stafford, 2001; Becht, Bolton and Roell, 2003). Only 14 (approximately 1%) of the 1,482 foreign takeovers included in my sample are hostile.

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CFIUS has discretion and jurisdiction over all commercial operations physically located in the U.S.18 Finally,

and again dissimilar from state-level antitakeover laws, there are no firm opt-out provisions embedded in

FINSA. Overall, FINSA is a more credible setting in which to examine the consequences of variation in the

takeover market due to the strength of the shock and the lack of firm discretion in responding to the shock.

2.2 A Primer on the Defense Production Act of 1950 and Amendments Enacted under FINSA

The Defense Production Act of 1950 permits the U.S. President to invoke emergency powers to prohibit

pending foreign transactions (transactions officially agreed upon by both the foreign investor and the target

firm) that threaten national security. To permit the administration to adapt to evolving forms of threat to

national security, national security is deliberately undefined in legislation and regulatory documents. However,

Moran (2009) describes three potential national security threats posed by foreign acquirers. First, excessive

reliance upon foreign-owned enterprises could render defense contractors vulnerable to supply chain

disruptions. Second, acquired technology could be deployed by the acquirer for other than commercial and

financial purposes, potentially enabling U.S. rivals. Third, the acquired entity could be used as conduit or

channel through which foreign entities could inhibit U.S. national interests (through, e.g., surveillance,

infiltration and sabotage).

President Gerald Ford formed CFIUS in 1975 to address threats to national security embedded in foreign

ownership and control of U.S.-based assets.19 Figure 4 illustrates the timeline of a typical CFIUS review. Notice

to the CFIUS of a pending deal is generally provided by either party to the transaction or a concerned industry

member or other U.S. entity, but the CFIUS may also self-initiate the review process. A 30-day CFIUS review

is initiated by a voluntary notice of an impending merger, acquisition or takeover. At its discretion, the CFIUS

will conduct a 45-day review, the outcome of which may be approval or approval conditional on an agreement

that mitigates national security concerns; the CFIUS may also recommend rejection to the U.S. President. The

President issues a decision in 15 days, though acquiring firms are much more likely to withdraw their offer

when rejection is recommended. The deliberations of the CFIUS, as a result of presidential privilege, are exempt

18 E.g., the 2013 acquisition of Nexen, a Canadian company with assets in the U.S., required CFIUS approval. 19 See https://www.treasury.gov/resource-center/international/foreign-investment/Pages/cfius-overview.aspx.

https://www.treasury.gov/resource-center/international/foreign-investment/Pages/cfius-overview.aspx

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from the Freedom of Information Act, and, as such, foreign investors and other observers lack opportunity to

learn about the U.S. foreign investment vetting process.

Public and Congressional dissatisfaction with foreign investments in U.S. spurred FINSA. Over 60

competing pieces of legislation in 2006 proposed erecting obstacles to foreign investment in sensitive industries

after CFIUS was perceived to have egregiously failed in its duty to protect the American public (Graham and

Marchick, 2006).20 The legislation that would become FINSA was first tabled on March 31, 2006. FINSA was

then approved by the House of Representatives on February 28, 2007. It was then revised and approved by the

Senate on June 29, 2007. The House of Representatives then approved the Senate version of FINSA on July

11, 2007. FINSA was signed into law by U.S. President through Executive Order #13,456 on January 23, 2008.

The Department of Treasury published the final FINSA implementing regulations on November 21, 2008, and

more detailed guidance regarding the policies governing the CFIUS post-FINSA was released on December 8,

2008, in the Department of Treasury’s Office of Investment Security publication titled, “Guidance Concerning

the National Security Review Conducted by the Committee on Foreign Investment in the U.S.” By the end of

2008, the CFIUS had received all mandate revisions.

Goldstein (2011), in evaluating FINSA, writes that the “CFIUS was significantly amended by (FINSA),

which increased congressional oversight, broadened the scope of factors for CFIUS to consider, and formalized

CFIUS’s practice of negotiating with the parties” (p.8). FINSA increased the purview of the CFIUS and foreign

investment frictions, first, by increasing the likelihood of an extended CFIUS investigation of a proposed

foreign takeover; second, by increasing U.S. Congressional involvement in the regulatory approval process and,

thereby, political uncertainty for foreign acquirers; third, by increasing the number of national security-related

concessions required from foreign acquirers before takeover approval; and, finally, by increasing the

20 CFIUS inactivity, manifested most visibly in the unobstructed transfer, later assailed by media and Congress, in U.S. port management from a British-held company to a company owned by the United Arab Emirates government, precipitated FINSA, which spurred the CFIUS and its members to greater scrutiny in evaluating and inhibiting foreign investment. See “Under Pressure, Dubai Company Drops Port Deal” at http://www.nytimes.com/2006/03/10/politics/under-pressure-dubai-company-drops-port-deal.html?_r=0. Retrieved October 7, 2016.

13

enforcement of, and penalties related to lapses in, commitments made by the foreign acquirer to mitigate

ongoing national security concerns. I describe these costs below.

First, Figure 1 illustrates a sharp increase in CFIUS investigations subsequent to the promulgation of

FINSA regulations by the Department of Treasury’s Office of Investment Security on November 21, 2008.

The increased investigation activity following from FINSA significantly increased the time to deal completion.

Lengthy approval periods are costly for foreign investors because they increase the probability of competing

bids (Jarrell and Bradley, 1980; Bhagwhat, Dam and Harford, 2016). As can be seen in Figure 1, prior to FINSA,

very few firms underwent a 45-day investigation after providing a voluntary notice to the CFIUS. Subsequent

to FINSA, 40% of firms that provided notice to the CFIUS underwent a 45-day investigation.

Second, and as indicated by Goldstein (2011), Congress more explicitly identified itself as the monitor of

the CFIUS and increased the CFIUS’s reporting requirements. Congress now requires the CFIUS to submit

reports on demand. FINSA provided an increase in Congressional oversight of the CFIUS that potentially

creates an increasingly politicized investment vetting process, which, in turn, increased political uncertainty.

Foreign investor concerns about political uncertainty are likely accentuated after FINSA due to the opaque

nature of CFIUS deliberations and no disclosures regarding the rationale for CFIUS recommendations to

approve, suspend or prohibit investment (Rose, 2014: 32).21 Adding to foreign investor uncertainties is that the

CFIUS is not time-barred from opening a review of any prior foreign investment, and the actions and findings

of the President through the CFIUS are not subject to judicial review (Li, 2016; p.190). Rose (2014) writes,

“CFIUS is not time-limited nor subject to a statute of limitations, which allows CFIUS to investigate a foreign

merger either upon a voluntary filing of notice by the parties to a transaction, or at any time, if the parties do

not file notice of the transaction voluntarily….” (p. 32).

21 Goldstein (2011) hints at new barriers to foreign investment in his statement, “…national security review in the U.S. has often become politicized, though primarily by the U.S. Congress and not by CFIUS. Politicized mergers result in uncertainty for businesses and can harm diplomatic relations with key trading partners” (p.1). The effect of Congressional involvement on foreign investor political uncertainty in the investment vetting process is further reflected by the nature of the ad hoc involvement of Congress in an unsuccessful bid for U.S. energy firm Unocal by Chinese firm CNOOC. After facing significant political headwinds in its bid for Unocal, CNOOC withdrew its bid, releasing the following statement: “The unprecedented political opposition…was regrettable and unjustified…. This political environment has made it very difficult for us to accurately assess our chance of success, creating a level of uncertainty that presents an unacceptable risk to our ability to secure this transaction” (Associated Press, 2005).

14

Third, along with the delayed acquisition timelines and increased political uncertainty foreign investors face

following FINSA, foreign investors also face a much greater likelihood of being burdened with costly mitigation

agreements.22 The costs of mitigation agreements are large. Many would-be foreign acquirers withdraw their

offers after learning that the CFIUS will review the deal.23 Of the 72 withdrawals from the CFIUS review

between 1995 and 2015, 65 occurred after FINSA (CFIUS Annual Reports). Regarding the fourth cost imposed

upon foreign acquirers by FINSA, Zaring (2010) writes, “CFIUS was given more penal authority.” FINSA

provided for the “imposition of civil penalties for any violation…, including [violations of] any mitigation

agreement” (p. 97). FINSA also encouraged the Committee to “develop and agree upon methods for evaluating

compliance with any agreement entered into or condition imposed with respect to a covered transaction that

will allow the Committee to adequately assure compliance,” which bolstered their sanctioning authority (p.97).

Rose notes that, “FINSA allows CFIUS to reopen reviews and investigations if there has been an intentional

breach of a mitigation agreement” (p.32).

Accountability for CFIUS mitigation agreements also increased among U.S. bureaucrats. Section 2(b) of

FINSA requires that the CFIUS must now provide a notice to Congress, which must be “signed by the

chairperson and the head of the lead agency, and shall state that, in the determination of the Committee, there

are no unresolved national security concerns with the transaction that is the subject of the notice or report.”

Because of this certification process, the members of the CFIUS must show that it has carefully evaluated and

22 The contents of mitigation agreements are sheltered by the CFIUS exemption from the FOIA, and, as such, a full listing of mitigation conditions is not available. However, Zaring (2010, p. 110) gleans a subset of mitigation conditions from disclosures by the Federal Communications Commission relating to the acquisition of U.S. telecommunication companies by foreign entities. Zaring identifies 28 such mitigation agreements placed upon foreign entities acquisitions of U.S. telecommunication firms between 1997 and 2007. For example, a state-owned Singaporean telecommunications company was required to ensure American citizen staffing, to permit inspections of accounting records and premises by U.S. agencies, to adopt and monitor strict visitation policies restricting foreign national access, and to undergo third-party auditing of the company’s network security protocols. Another example of a mitigation agreement was a CFIUS requirement that Lenovo, in its 2005 purchase of IBM’s personal computer business, be prohibited from knowing the identity of federal government customers and that it seal off from Chinese employees two buildings in a North Carolina office park (Zaring, 2010; p .109). Byrne (2015) sheds light on another example. Senate testimony revealed that, prior to FINSA, the CFIUS approved the previously mentioned transfer of U.S. port management to a United Arab Emirates-controlled firm, requiring that the company mandatorily commit to various security initiatives and that the firm provide the U.S. government with unlimited access to all written records about its U.S. operations without a warrant (p. 877). 23 E.g., “Tsinghua Unisplendour, a Chinese state-controlled company, dropped plans to buy 15% of Western Digital, an American maker of computer hard-drives, for $3.8 billion. The Chinese withdrew after the Committee on Foreign Investment in the United States, a government body, said it would review the deal.” The Economist (Espresso), February 24th, 2016.

15

responded to potential national security risks; as a result, mitigation agreements are common after FINSA. In

summary, increased enforcement of mitigation agreements unambiguously increased the cost of foreign

investment because mitigation agreements now “have the effect of law” and the breaking of a mitigation

agreement can result in monetary penalties up to the full value of the transaction and may potentially force the

unwinding of the transaction (Rose, 2014; p. 14).

The post-FINSA change in the severity of CFIUS scrutiny is reflected by large increases in media coverage

of CFIUS activities. Though the CFIUS is not obligated to disclose its activities, traces of CFIUS involvement

in commerce are still captured by the media. Figure 2 shows that articles mentioning both “CFIUS” and

“withdraw” (as in, “withdrawn offer”) between 1989 and 2005 numbered in the dozens while, since 2005, there

have been more than 350 such articles. Overall, features of FINSA and anecdotal evidence of the many

additional and meaningful costs placed on foreign acquisition activity demonstrate the impact of FINSA on the

takeover market.

In empirical analyses, Godsell et al. (2017) find that the likelihood of takeover for a FINSA-affected firm

likely to be taken over pre-FINSA declined significantly after FINSA was passed; and that the market reaction

to legislative events preceding FINSA were negative and economically large. They find FINSA-affected firms

are 74% less likely to be subject to a takeover after FINSA relative to the control group of firms unaffected by

FINSA. This result is consistent with a significant decline in takeovers subsequent to FINSA for FINSA-

affected firms. Godsell et al. (2017) further find that both findings are amplified for firms that were more likely

to be taken over pre-FINSA. This evidence is an important prerequisite for my analysis because, as Cain et al.

(2017) show in an empirical study with data spanning 65 years, an attenuation in takeover activity is not found

in the state antitakeover law setting. Overall, findings reported by Godsell et al. (2017) are consistent with

increased frictions for foreign investment for FINSA-affected firms following FINSA. This result shows that

the FINSA setting is appropriate for examining firm-level responses to an exogenous change in the takeover

market.

16

Consistent with FINSA attenuating the U.S. takeover market, I expect that earnings management is

impacted in FINSA-affected firms subsequent to FINSA through one of the five aforementioned channels.

This leads to my first hypothesis, stated in alternate form:

H1: FINSA-affected firms record more income-increasing accruals after FINSA. A corollary following from H0 is that I expect any effect of the takeover market on earnings management

to be concentrated in firms that were more likely to be subject to foreign takeover pre-FINSA. This logic

follows from the intuition that those firms most likely to be subject to takeover will be most affected by changes

in the takeover market.

3.0 DATA AND RESEARCH DESIGN

3.1 Hand-Collected Data

The CFIUS provides summary statistics of its activity each year, but does not disclose, and is not

required to disclose, the firms that undergo the CFIUS investment approval process. Despite this opacity

regarding the firms engaged in the CFIUS process, firms engaged in the CFIUS approval process at times

disclose the engagement with stakeholders through SEC regulatory filings and, less frequently, through news

articles. I gather data on firms directly affected by the CFIUS investment approval process. Cataloging the firms

that disclose CFIUS engagement provides a partial panel of the firms affected by the CFIUS (partial because

not all firms will disclose CFIUS engagement in SEC filings and because not all deals warrant media coverage).

Three sources were used to identify CFIUS firms and to populate the coding panel: Mergent Online, Factiva

and Lexis-Nexis. I define my the treatment group of FINSA-affected as the firms in the same 4-digit SIC as

the firms whose takeover was subject to CFIUS scrutiny.

I draw SEC filing data from Mergent Online. All government filings spanning January 1, 1990–January 31,

2014 are examined for mentions of “Committee on Foreign Investment,” “CFIUS,” “FINSA” or “Foreign

Investment and National Security Act.” This search generated 6,865 SEC filings. I individually inspect each

SEC filing to identify the context in which the CFIUS is mentioned and code accordingly. Data collected

included filing type (103 distinct filing types are examined, e.g., 10-K, DEF 14A, PREM 14A), filing date,

company name of the filer, company name of the merger/acquisition counterparty, string of text that formed

17

the basis of the coding decision, and coding decision. I then manually match each filer name to

Compustat/CRSP, and firm characteristics are collected.

I draw press release data from Factiva and Lexis-Nexis in separate and redundant processes to ensure

full coverage of media sources. All media articles spanning January 1, 1990–January 31, 2014 are examined for

mentions of “Committee on Foreign Investment” or “CFIUS.” This search generated 6,232 articles in Factiva

and 898 results in Lexis-Nexis. Each article is individually inspected to identify the context in which the CFIUS

is mentioned and coded accordingly. Data collected included the media source (e.g., the Wall Street Journal),

the date of the article, the company name mentioned in the article, the company name of the merger/acquisition

counterparty, the title of the article, and the coding decision. The SEC filing panel and the media article panel

are then merged to generate a list of firms and a series of dates during which each firm was engaged in various

stages of the CFIUS process. Each filer name is then matched to Compustat. Overall, from the 13,995 articles

and SEC filing records, I identify 3,539 references to CFIUS made by 557 unique firms.

In Table 1, I ascertain the extent to which my hand-collected panel of CFIUS-affected firms covers

the total number of firms scrutinized by CFIUS. This comparison is subject to at least two caveats. The legal

status of a firm (public or private) is irrelevant to CFIUS. Therefore, it is virtually certain that the a review of

SEC filings and media articles will not capture the full set of CFIUS-affected firms as private firms are much

less likely to warrant media attention or to file with the SEC. Consequently, the firm-level panel is tilted toward

public firms and so captures only a subset of the all firms engaged in the CFIUS review process. Second, my

estimation panel spans 1990 to 2014, yet CFIUS activity data is only reported as of 1995 (Graham and Marchick,

2006). Consequently, the extent to which my hand-collected data captures CFIUS activity can only be evaluated

in years after 1995.

As shown in column 2 of Table 1, the CFIUS performed 1,846 30-day reviews and 355 subsequent 45-

day investigations. I am able to identify 664 media or SEC filing referrals to a submission of notice to, or

commencement of review by, CFIUS. The overlap varies considerably by year with a high of 45% in 2015 and

a low of 5% in 2002. The average coverage in the pre-FINSA period is 24% and 35% in the post-FINSA period.

18

Higher coverage in the post-FINSA period is consistent with CFIUS scrutiny carrying greater risk and costs

for the scrutinized firm.

Relatively few firms provide additional disclosure regarding the CFIUS process after publicizing the

initial interaction with CFIUS. For example, and as reported in columns 3 and 4 of Table 1, though CFIUS

undertook 355 investigations during my sample period, only 29 firms disclosed that CFIUS reviews had

transitioned to investigations. Relatively fewer firms report withdrawals from the CFIUS program. I am able to

identify only four of the 72 firms that withdrew from the CFIUS process during the sample period.

Insert Table 1 about here

I use my hand-collection of CFIUS-affected firms to create my treatment group of FINSA-affected

firms. Ex ante, takeovers subject to CFIUS scrutiny are expected to face more frictions and uncertainty than

unscrutinised firms. To identify variation in CFIUS scrutiny, I classify as scrutinized any firms in the 4-digit

SIC code of firms that interacted with the CFIUS on more than five occasions (N = 20,070). These firms

comprise the treatment group whose accrual behavior I examine to test my hypothesis.

3.2 Firm Characteristics

Firm-level financial statement data spanning 1990–2014 are drawn from Compustat Fundamentals Annual

File. Institutional investor and analyst following variables are drawn from Thomson Reuters 13F and IBES,

respectively. Table 2 shows firm-year observations at each stage of the data screening process, starting with

data retrieval and ending with my estimation sample. I begin with the Compustat universe of 120,738 firm-

years spanning 1989 to 2015. I exclude 4,201 firm-year observations with 2-digit SIC values between 60 and 69

(inclusive); 54,279 firm-year observations with missing test or control variables; 5,133 firm-years in 2-digit SIC

– year clusters with fewer than 20 firms; and 24,207 firm-years without a pre-FINSA (2007) takeover probability

score. I use a panel of 32,740 firm-years to test H1. Approximately one-third (10,938) of firm-years are FINSA-

affected. In subsequent testing, I partition firms based on the pre-FINSA likelihood of takeover. Within the

low takeover likelihood partition, 5,330 (11,046) are FINSA-affected (unaffected by FINSA). Within the high

takeover likelihood partition, 5,608 (10,756) are FINSA-affected (unaffected by FINSA)


19

Panel A of Table 3 shows that approximately 50% of treatment firms are in high-tech industries. Energy,

healthcare, manufacturing and utility firms round out the balance of firms in each of the three treatment groups.

In Panel B of Table 3, I find an increasing number of firm-year observations over time because I require firms

have a pre-FINSA takeover probability score and firms must exist in 2007 in order to receive a score. Firms

early in the period are less likely to exist in 2007 so fewer firms are included from the early half of the sample.

My initial data retrieval captures data spanning 1989 to 2015 while in my estimation sample firm-years span

1991 to 2013. The requirement that firm-year observations have non-missing two-year lead and lagged variables

(e.g., standard deviation of earnings, stock volatility) drive this temporal truncation. Finally, my results are

insensitive to a variety of shortened windows around FINSA within the 1991 to 2013 window (e.g., 5 years

before and after FINSA [2003 – 2013].


3.3 Research Design

My research question asks whether the takeover market affects managers’ accrual choices. The prior

literature has examined state-level antitakeover laws and firm-level antitakeover provisions to address this

question. Motivated by the flaws identified in these setting (see, e.g., Karpoff and Wittry, 2017), I examine a

new and more plausible exogenous source of variation in the takeover market.

3.3.1 Accruals

Following Francis, Michas and Seavey (2013) and Gormley and Matsa (2014), the residual of the performance-

adjusted (Kothari, Leone and Wasley, 2005) modified-Jones (1991) model (Dechow, Sloan and Sweeney, 1995)

is my proxy for discretionary accruals. The following model is estimated after removing firm-year observations

in 2-digit SIC – year clusters with fewer than 20 observations.

Total Accrualsit = B0 + B11

𝐴𝑇𝑖𝑡 + B2(ΔREVit – ΔARit) + B3PPEit + B4ROAit + Industry and Year Fixed Effects + εit (1)

Where Total Accruals are calculated from statement of cash flow data as earnings before extraordinary items

(IBC) less cash flows from operations (OANCF) (Collins and Hribar, 2002; Bushman, Lerman and Zhang,

20

2016; Hui, Nelson and Yeung, 2016). ∆REVit is the change in sales (SALE) from year t-1 to year t; ∆ARit is the

change in accounts receivable (RECT) from year t-1 to year t; PPEit is gross property, plant and equipment

(PPEGT); and ROAit is return on assets calculated as the income before extraordinary items (IB) divided by

lagged total assets (ATt-1). All variables are deflated by lagged total assets and all continuous variables are

winsorized at 1% and 99%. 𝜀𝑖𝑡 is an error term capturing unexplained accruals. The error term is my firm-year

measure of discretionary accruals. Standard errors are clustered by 2-digit SIC.

I test H1 by regressing discretionary accruals (DACC) on determinants of discretionary accruals and my

DiD test variables, Treatment Industry, Post-FINSA and Treatment Industry × Post-FINSA.

DACCit = B0 + B1Treatment Industryit + B2PostFINSAit + B3Treatment Industryit × PostFINSAit + B4-22Controlsit + Firm &

Year Fixed Effects + εit (2)

The test variable is Treatment Industry × PostFINSA. Though I include Treatment Industry and PostFINSA in

Equation (2), these variables are subsumed by firm and year fixed effects during estimation. I include firm fixed

effects to account for substantial heterogeneity in the accrual generating process across firms (Owens, Wu and

Zimmerman, 2017). I also include year fixed effects to mitigate concerns about cross-sectional dependence and

time-invariant firm characteristics. To account for time-varying firm characteristics known to be related to

firms’ accrual choices, I include control variables for lead, lagged and contemporaneous cash flows (Dechow

and Dichev, 2002), firm size and sales growth (Jones, 1991), leverage (DeFond and Jiambalvo, 1994; Barton

and Waymire, 2004), sales volatility (Francis, LaFond, Olsson and Schipper, 2004), lagged net operating assets

(Hirshleifer, Hou, Teoh and Zhang, 2004) and market return (Zhang and Zhuang, 2012). These variables

capture the mechanical effects of financial performance on discretionary accruals. All variables are defined in

Appendix A. Standard errors are clustered by firm.

If management entrenchment theory describes the relation between the takeover market and earnings

management, I expect that earnings management will increase (𝛽3 > 0) subsequent to the passage of FINSA for

firms most likely to be subject to the takeover market. If quiet life, alignment, career concern or signalling

theory describes the relation between the takeover market and earnings management, I expect that earnings

21

management will decrease subsequent to the passage of FINSA for firms most likely to be subject to the

takeover market (𝛽3 < 0).

3.3.2 Takeover prediction model

I predict that any takeover market effect on accrual decisions will most affect those firms more likely to be

taken over prior to FINSA. I test my expectation with a triple-DiD test. To characterize firms’ pre-FINSA

takeover likelihood I estimate a takeover prediction model, following Cremers, Nair and John (2009), Karpoff,

Schnolau and Wehrly (2017) and Godsell et al. (2017). I estimate Equation (3) using only pre-FINSA data

spanning 2000-2008 to generate the pre-FINSA predicted probability of a foreign takeover for each firm-year.24

Takeoverit = B0 + B1-14Takeover Determinant Variables +State × Year Fixed Effects + Industry × Year Fixed Effects + εit (3)

The dependent variable is equal to one if a foreign takeover of firm i is completed in year t and zero

otherwise. Takeover determinant variables include size, book leverage, market-to-book ratio, property ratio,

liquidity ratio, sales growth, employee growth, return on assets, market return, sales concentration, foreign sales,

foreign incorporation, number of analysts following and institutional ownership. All variables are defined in

Appendix A. State of incorporation by year fixed effects are included to control for pre-existing state-level

antitakeover laws and related court cases that may cause variation in the probability of firm takeover across

states. State of incorporation values recorded in Compustat are known to report the state of incorporation for

the most recent firm-year only so I use historical state incorporation data gathered from historical SEC filings.25

Industry (4-digit SIC) by year fixed effects are included to control for time-varying variation in takeover activity

across broad industry groups. Standard errors are clustered by state-year. I use this model to predict firms’

takeover likelihood and I place firms with a below- (above-) median takeover likelihood into the low (high)

takeover probability partition based on firms’ 2007 predicted takeover probability score. I expect any effect of

the takeover market on accrual decisions to be stronger in the high takeover likelihood partition.

24 I run my prediction model until 2006 because FINSA legislation was passed by U.S. Congress and Senate in 2007, was implemented by the Department of Treasury in 2008 and became effective in 2009. Consequently, FINSA may have affected takeover activity as soon as it was passed in 2007, even though the costs related to its enactment were not observable until 2009. Results are similar if I include all sample years in the takeover prediction model. 25 I am grateful to Jared Smith for providing historical state incorporation data.

22

Because I focus much of my analysis on firms that are more likely subject to takeover, a concern is that

takeover determinants used to identify high and low takeover probability firms are correlated with discretionary

accruals. For example, there is evidence of associations between accruals and market-to-book ratio, employee

growth and external monitoring (institutional ownership, analyst following) (Collins, Pungaliya and Vijh, 2017;

Allen, Larson and Sloan, 2013; Rajgopal, Venkatachalam and Jiambalvo, 2002; Banker, Fang and Jin, 2015).To

mitigate concerns related to correlated omitted variables, I estimate Equation (2) after adding all control

variables specified in my takeover model. If firm characteristics associated with a greater likelihood of takeover

are driving elevated accruals in the high takeover probability partition, then my results should be subsumed by

the inclusion of these variables. These control variables include the aforementioned market-to-book ratio,

property ratio, liquidity ratio, return on assets, HHI, the ratio of foreign sales to total sales, the number of

analysts following the firm and institutional ownership.

4.0 RESULTS

4.1 Summary Statistics

Summary statistics for variables included in the foregoing equations are provided in Table 4. The mean of

total accruals for the treatment (control) sample is −7.9% (–5.4%) of total assets, signed discretionary accrual

measures approximate zero while the absolute value of discretionary accruals is 6.7% (5.8%) of total assets.

Treatment and control characteristics will naturally differ because the treatment group is defined by industry.

Firms in different industries have dissimilar financial characteristics necessitating careful inclusion of an

exhaustive suite of financial characteristics that affect accrual decisions. Operating cash flow is 6.5% (6.9%) of

total assets, book leverage is 19.1% (20.7%), sales growth is 6.2% (8%) and market return is 24.3% (21.6%). I

conduct multicollinearity diagnostic tests for the independent variables in Equation (2). Multicollinearity does

not appear to be a concern with the inverse of the variance inflation factor in excess of 0.1 for all variables.


4.2 Earnings Management Results

Columns 1 and 2 of Table 5 report the results of tests of H1. H1 predicts variation in earnings management

for FINSA-affected firms after FINSA implementation relative to firms unaffected by FINSA. While most of

23

my tests regress discretionary accruals on my test and control variables, I begin my analysis by regressing total

accruals on all accrual determinants in the performance-adjusted (Kothari et al., 2005) modified Jones (1991)

model (Dechow, Sloan and Sweeney, 1995). Concerns that two-step models are mispecified (Chen, Hribar and

Melessa, 2018) motivate this test. Column 1 (2) examines the behavior of total (discretionary) accruals for

FINSA-affected firms around FINSA. I further address concerns raised by Chen et al. (2018) by including in

column 2 independent variables from the performance-adjusted (Kothari et al., 2005) modified Jones (1991)

model (Dechow, Sloan and Sweeney, 1995). Year (firm) fixed effects are included and these effects subsume

main effects for PostFINSA (Treatment Industry).


The coefficient for the variable testing H1, Treatment Industry × PostFINSA, is positive, and economically

and statistically significant and economically significant in column 1 (2). The result in column 1 (2) suggests

that FINSA-affected firms record more income-increasing discretionary accruals, relative to control firms, after

FINSA equal to 0.42% (0.43%) of total assets, or approximately 8.5% (8.9%) of average ROA (4.8%) for the

total sample. The economic magnitude of these empirical findings are plausible, in contrast with inferences

drawn from underspecified discretionary accrual models (see Ball [2013], Zimmerman [2018], and Jackson

[2018] for a discussion). According to a recent survey, 20% of firms manage earnings to distort earnings and

the magnitude of the distortion is around 10% of earnings (Dichev, Graham, Harvey and Rajgopal, 2013). I

attribute the plausibility of the economic magnitude of these results (vis-à-vis, e.g., Jones, 1991) to the extensive

suite of Equation (2) control variables explaining normal variation in accruals.

I next re-estimate Equation (2) within the takeover probability partitions and present the results in Table

6. I examine the one- (two) step discretionary accruals model in columns 1 and 2 (3 and 4) of Table 6. Results

drawn from the sample partitioned on takeover probability are less ambiguous. I observe no change meaningful

change in discretionary accruals following FINSA for FINSA-affected firms for firms in the low takeover

probability partition. The coefficients reported on the test variable in columns 1 and 3 are of mixed sign,

statistically insignificant and economically small. In contrast, the coefficient on the test variable in the high

takeover probability partition, reported in columns 2 and 4, are positive, economically large and statistically

24

significant. Overall, accrual decisions by managers of FINSA-affected firms less likely to be subject to takeover

appear unaffected by FINSA. In contrast, managers of FINSA-affected firms more likely to be subject to

takeover appear to record more income increasing discretionary (or fewer income decreasing) accruals after

FINSA. This finding rejects H1 and establishes support for the managerial entrenchment hypothesis.


Given the very similar results generated by the one- and two-step accrual model specifications reported in

Tables 5 and 6, I infer that specification issues raised by Chen et al. (2018) have a muted effect in my setting.

Consequently, for brevity, I present results using the two-step model in subsequent tests. I replicate all tests

using the one-step model in an Online Appendix and find all results are similar using the one-step model.

4.3 Parallel Trends Assumption

The validity of DiD research designs rests on the assumption that control firms are a suitable counterfactual

for treatment firms (Abadie, 2005). A suitable counterfactual control group should reflect the behavior of the

treatment group ‘but for’ the treatment. Parallel pre-treatment trends in the dependent variable supports validity

of the control group as a counterfactual for the treatment group (Roberts and Whited, 2013; Atanasov and

Black, 2015). I test whether discretionary accruals exhibit parallel pre-treatment trends by estimating a DiD

research design similar to my main test with the exceptions that I 1) drop all post-treatment (post-2008) years

and 2) define a placebo treatment year of 2005. I estimate Equation 2 after replacing the Post-FINSA with the

placebo event year indicator, Post-Placebo. Post-Placebo is an indicator variable equal to 1 in years after 2005 and

zero otherwise.

This test determines whether accruals differed for treatment firms from the control group in the years

leading up to the treatment. If so, the parallel trends assumption is violated in my setting and a DiD research

design is an inappropriate tool for answering my research question. In contrast, if the parallel trends assumption

is valid in my setting I should observe statistically insignificant coefficients on the test variable, Treatment Industry

× Post-Placebo, indicating that the pattern of pre-treatment accruals for the treated firms is similar to the pattern

of pre-treatment accruals for the control firms. The results are presented in Table 7. Column 1 and 4 present

results for the full sample. Columns 2 and 5 (3 and 6) report results for the low (high) takeover probability

25

partition. Coefficients on the test variable, Treatment Industry × Post-Placebo, are statistically insignificant across

all columns in Table 7. These results validate the parallel trends assumption necessary for my DiD and triple-

DiD analysis by showing that the post-treatment effect I observe in my main results is not due to a trend in the

pre-period.


4.4 Decomposed Discretionary Accruals

I observe a net increase in income-increasing discretionary accruals, on average, for FINSA-affected firms

after FINSA, relative to the control group of firms unaffected by FINSA. Firms with a higher pre-FINSA

probability of takeover drive this effect. However, it is unclear whether more income-increasing or fewer

income-decreasing discretionary accruals drive this effect. To better understand the effect of the takeover

market on discretionary accruals I re-estimate Equation (2) after replacing signed discretionary accruals with 1)

the absolute value of unsigned discretionary accruals, 2) the absolute value of positive discretionary accruals

and 3) the absolute value of negative discretionary accruals. I present the results in Table 8.

The first two columns of Table 8 replicate the findings reported in columns 3 and 4 of Table 6. Those

findings showed strong support for greater income-increasing discretionary accruals recorded by FINSA-

affected firms after FINSA relative to a control group of firms unaffected by FINSA. Results presented in

columns 3 and 4 re-estimate Equation (2) after replacing signed discretionary accruals with the absolute value

of unsigned discretionary accruals. The coefficient on the test variable, Treatment Industry × Post-FINSA, in the

low takeover probability partition reported in column 3 is negative, economically small and statistically

indistinguishable from zero. In contrast, the coefficient on the test variable in the high takeover probability

partition reported in column 4 is negative, economically large and statistically significant. The coefficient on the

test variable in column 4 suggests FINSA-affected firms recorded fewer discretionary accruals after FINSA

relative to firms unaffected by FINSA.

This finding corroborates similar findings in the prior literature suggesting that firms that adopt more firm-

level antitakeover provisions or firms subject to stronger antitakeover state laws record fewer discretionary

accruals (Armstrong, Balakrishnan and Cohen, 2012; Zhao and Chen, 2008a, 2008b, 2009). However, when

26

interpreted as evidence that managers indulge in the quiet life, this evidence conflicts with the findings reported

in Table 5 suggesting more FINSA-affected firms record more income-increasing discretionary accruals after

FINSA. That is, the results suggest more income-increasing discretionary accruals, but fewer discretionary

accruals.

I address this paradox by decomposing discretionary accruals into positive discretionary accruals and

negative discretionary accruals because fewer negative discretionary accruals could generate the appearance of

more income-increasing discretionary accruals while also leading to fewer discretionary accruals overall. I

present the results for positive discretionary accruals in columns 5 and 6. I present the results for negative

discretionary accruals in column 7 and 8.


The coefficient on the test variable in columns 5 and 6 in Table 8 are statistically indistinguishable from

zero in either the low or high takeover probability partition. This result suggests that FINSA-affected firms

recorded positive discretionary accruals after FINSA that are indistinguishable from those recorded by firms

unaffected by FINSA. I next estimate Equation (2) after replacing signed discretionary accruals with the

absolute value of negative discretionary accruals. The test variable coefficient reported in the low probability

partition in column 7 is indistinguishable from zero. In contrast, the coefficient on the test variable in the high

takeover probability partition is negative, economically large and statistically significant. This result suggests

that FINSA-affected firms boosted income by recording fewer negative discretionary accruals after FINSA,

relative to firms unaffected by FINSA.

This finding has at several implications. First, fewer income-decreasing accruals suggest that FINSA-

affected firms may also record fewer income-decreasing special item. Second this finding suggests FINSA-

affected firms may also record fewer write-down accruals after FINSA. Third, fewer income-decreasing accruals

suggest that FINSA-affected firms are recognizing less bad news in their financial statements relative to firms

unaffected by FINSA. I test these implications in a triple-DiD research design.

First, I examine accruals related to special items. I replace the dependent variable in Equation (2) with

variants of special items and re-estimate Equation (2), and present the results in Table 9. In columns 1 and 2, I

27

regress signed special items on Treatment Industry × Post-FINSA. The coefficient on the test variable in the low

takeover probability partition is indistinguishable from zero. The coefficient on the test variable in the high

takeover probability partition, reported in column 2, is positive, economically large and statistically significant.

To determine whether the net effect of income-increasing special items are the product of an increase in

income-increasing special items or fewer income-decreasing special items, I decompose special items into the

absolute value of positive special items and the absolute value of negative special items. I examine the

decomposed positive and negative special items in their original low or high takeover probability partitions. I

present the results in columns 3 to 6 of Table 9.


The sample size of firms recording income-increasing special items is approximately one-third that of the

sample size of those firms recording income-decreasing special items. Overall, income-increasing special items

are statistically indistinguishable from zero in both low and high takeover probability partitions reported in

columns 3 and 4. The change in income-decreasing special items is similarly indistinguishable from zero in the

low takeover probability partition, as reported in column 5. In contrast, the coefficient on the test variable,

Treatment × Post-FINSA, is negative and statistically significant in column 6 of Table 7, suggesting a decrease in

income-decreasing special items for FINSA-affected firms after FINSA, relative to firms unaffected by FINSA.

This evidence is consistent with the results reported in Table 8 that suggest that managers of FINSA-affected

firms record fewer income-decreasing accruals after FINSA, relative to firms unaffected by FINSA.

A common income-decreasing accrual is the write down accrual. Write-downs are universally negative in

my sample so, unlike special items and discretionary accruals, I do not decompose write-downs into positive

and negative values. I replace the dependent variable in Equation (2) with write-downs and re-estimate Equation

(2), and present the results in columns 7 and 8 in Table 9. I use the absolute value of write-downs in my analysis

for expositional ease. I find the coefficient on my test variable, Treatment Industry × Post-FINSA, is statistically

indistinguishable from zero in the low takeover probability partition reported in column 7 of Table 9. In

contrast, the coefficient on the test variable is negative and statistically significant in the high takeover

probability partition reported in column 8 of Table 9. This result suggests that managers of FINSA-affected

28

firms recorded fewer write-downs after FINSA relative to firms unaffected by FINSA. Overall, the results

presented in Table 9 provide account-level evidence that managers of FINSA-affected firms recorded fewer

income-decreasing accounting accruals after FINSA, relative to firms unaffected by FINSA.

Fewer income-decreasing accruals have implications for the timeliness of bad news accounting information

in the market. If firms affected and unaffected by FINSA experience a similar frequency and magnitude of bad

news events after FINSA, then fewer income-decreasing accruals should lead to lower levels of financial

reporting conservatism in FINSA-affected firms after FINSA, relative to firms unaffected by FINSA. I test this

prediction using the Basu (1997) earnings-return model and report the results in Table 10. The Basu model

regresses earnings on returns and allows the return coefficient to vary with the sign of the return. The base

Basu model is:

NIit = B0 + B1NEGit + B2RETit + B3NEG × RETit + εit (4)


NI is income before extraordinary items (IB) for firm i in year t divided by market value. RET is the buy-

and-hold stock return of firm i over year t. NEG is an indicator variable equal to one if RET is negative, and

zero otherwise. B2 captures the timeliness of earnings with respect to good news. B3 captures the asymmetric

timeliness of bad news relative to good news and reflects firms’ financial reporting conservatism. I nest my

DiD research design into the Basu earnings-return model by interacting Treatment Industry, Post-FINSA and

Treatment Industry × Post-FINSA with the independent variables in Equation (4) to form Equation (5). Following

the prior literature (e.g., Ramalingegowda and Yu, 2012), I add the main and interaction effects for control

variables including stock volatility, market value, market-to-book ratio and book leverage. I present the results

in Table 10.

NIit = B0 + B1NEGit + B2RETit + B3NEG × RETit + B4Treatment Industry + B5PostFINSA + B6Post-FINSA × NEG

+ B7Post-FINSA × RET + B8Post-FINSA × NEG × RET + B9Treatment Industry × PostFINSA + B10Treatment Industry ×

NEG + B11Treatment Industry × RET + B12Treatment Industry × Post-FINSA × NEG + B13Treatment Industry × Post-FINSA

× RET + B14Treatment Industry × Post-FINSA × NEG × RET + B15-18Controls + B19-22Controls × RET + B23-26Controls ×

NEG + B27-30Controls × NEG × RET + Firm & Year Fixed Effects + εit (4)

29

Column 1 of Table 10 presents the Basu conservatism model estimated for the full sample. Column 2 (3)

presents the results when estimating the Basu conservatism model for the low (high) takeover probability

sample. The test variable is the interaction variable, Treatment Industry × Post-FINSA × RET × NEG. The test

variable coefficient in column 1 is negative and statistically significant. This result provides evidence that the

level of financial reporting conservatism for FINSA-affected firms declines after FINSA, relative to control

firms. The coefficient on the test variable is indistinguishable from zero in column 2, which estimates Equation

(4) in the low takeover probability sample. In contrast, the test variable coefficient on the test variable in column

3, which estimates Equation (4) in the high takeover probability sample, is negative and statistically significant.

These DiD and triple-DiD result provides strong evidence that the takeover market affects financial reporting.

FINSA affected firms more likely to be affected by FINSA have lower levels of financial reporting conservatism

after FINSA, relative to a control group of high takeover probability firms unaffected by FINSA.

4.5 Robustness Tests

I use discretionary accrual models to test H1. I perform several robustness tests to address recent concerns

regarding discretionary accrual model misspecification.

4.5.1 Added Control Variables

First, underlying economic circumstances uncaptured by accrual models may spuriously generate

discretionary accruals in discretionary accrual models. For example, discretionary accruals need not reverse in

fiscal years adjacent to their origination and Larson, Sloan and Giedt (2018) show that lead and lagged cash

flows, beyond a one-year lag or lead, help explain normal accruals. I add two-year lead and lagged cash flow

variables to Equation (2) to ensure model misspecification does not drive my results. Another source of model

misspecification is economic volatility. Economic volatility generates noise in the accrual generating process

and lead to spurious inferences because volatile but genuine economic fundamentals may manifest as

discretionary accruals in the Jones (1991) model. For example, Owens, Wu and Zimmerman (2017) find that

discretionary accruals models that do not control for idiosyncratic risk are mispecified. To mitigate the risk that

the discretionary accrual model classifies legitimate accruals as discretionary accruals, I generate discretionary

accruals using the performance-adjusted (Kothari et al., 2005) modified Jones (1991) model (Dechow, Sloan

30

and Sweeney, 1995) and further control for return on assets in second stage regressions reported in Table 5 to

10.

To further mitigate concern that discretionary accruals reflect idiosyncratic risk and volatility in the accrual

generating process I include variables capturing operating cycle, stock volatility, the standard deviation of

earnings, the standard deviation of cash flows. I also control for economic losses as in Ball and Shivakumar

(2006) by including controls for industry-adjusted cash flows (firm cash flows minus median industry cash flows

[2-digit SIC]), an indicator variable set to one if industry-adjusted cash flows are less than zero, and zero

otherwise. I re-estimate Equation (4) after including control variables controlling for various forms of volatility,

along with all previously used control variables, and present the results in Table 11.


Increases in Adjusted R-squared values reflect the power of these additional control variables to explain

accrual behavior. Adjusted R-Squared values increase by between 3 and 13 percentage points across the eight

columns of Table 11 relative to corresponding columns in Table 8. Results presented in Table 11 generally

corroborate inferences drawn from Table 8.

The test variable coefficient in column 2 provides corroborating evidence of a net increase in income-

increasing discretionary accruals for FINSA-affected firms in high takeover probability firms. After including

these additional control variables, I find no change in discretionary accruals, as reported in columns 3 and 4. I

find no change in positive discretionary accruals. I find corroborating evidence of a large decline in income-

decreasing discretionary accruals after FINSA for FINSA-affected firms. These results show my main results

are robust to controlling for additional variables that explain variation in accruals related.

4.5.2 Firms That Lobbied FINSA Legislators

The prior literature criticizes exogeneity claims made by those using the state-antitakeover law because

many firms are thought to have influenced the antitakeover legislation adopted by the states in which they are

incorporated. Gartman (2000) reports that 49 state antitakeover laws in 23 different states were subject to firm

lobbying that favored passage. Despite the small number of lobbying firms vis-à-vis the sample sizes used in

the prior literature, Karpoff and Wittry (2017) find that retaining these lobbying firms has a material impact on

31

inferences drawn from analyses using state antitakeover laws. Consequently, I replicate my analyses by varying

the inclusion of the 13 domestic firms I have identified as having lobbied for or against FINSA. The lobbying

firms were Boeing Company, Carlyle Group, Conoco Philips, EDS Corporation, Exxon Mobil, General

Electric, Goldman & Sachs, Halliburton, JP Morgan Chase, Lehman Brothers, Merrill Lynch, United

Technologies Corporations and Xcel Energy. Results excluding these companies are presented in Table 12.

Inferences drawn after excluding lobbying firms are nearly identical to those drawn from my main specification.


4.5.3 Exclusion of Financial Recession Years

To address concerns that results stem from the financial recession, I exclude fiscal years 2007 and 2008.

These results are presented in Table 13 and corroborate all prior inferences.


4.5.4 Placebo (Randomized Treatment) Test

To mitigate additional concerns that my results are spuriously generated, I scramble my definition of my

treatment group and re-estimate Equation (2). I redefine treatment firms as equal to one for a random selection

of firm-years comprising 50% of the population of firms in my sample and zero otherwise. I re-estimate the

models presented in Table 8 and tabulate my results in Table 14. The coefficient on the test variable, Placebo

Firm × Post-FINSA, is of mixed sign and statistically insignificant across all models. This result helps to rule out

alternative explanations for my findings.


4.5.6 Discretionary accruals and ex post manipulation

Discretionary accruals are positively associated with ex post indicators of earnings management including

AAERs, class action lawsuits and restatements, as well as the frequency with which firms meet or beat analyst

earnings forecast (McNichols and Stubben, 2018). In untabulated analyses, I examine the association between

my test variable, Treatment × Post-FINSA, the frequency of meeting or beating earnings forecasts, AAERs and

restatements. I find a positive and statistically significant correlation (0.0629) between my test variable and the

frequency of meeting or beating earnings, but only in the high takeover probability partition. However, this

32

correlation disappears in multivariate analysis including control variables use in Equation (2). I find no

association between my test variable and restatements. I find a negative and statistically significant univariate

correlation between my test variable and AAERs. However, this correlation spans both firms in the low and

high takeover probability partitions, and consequently, is likely due to factors unrelated to my analyses

Furthermore, Karpoff, Koester, Lee and Martin (2017) note severe empirical issues with the AAER database

which obfuscate inferences from correlations between discretionary accruals and AAERs. Overall, I find little

evidence of ex post manipulation related to the variance in discretionary accruals caused by FINSA. Lack of

evidence may be due to the nature of the variance in discretionary accruals. My evidence points to fewer income-

decreasing discretionary accruals rather than more income-increasing discretionary accruals. It may be there is

an asymmetric level of association between signed discretionary accruals and ex post outcomes. This is plausible

because outsiders may find it more difficult to detect the absence of income-decreasing accruals vis-à-vis the

recording of income-increasing accruals.

4.5.7 Alternative Discretionary Accrual Models

I use the residual from the performance-adjusted (Kothari et al., 2005) modified Jones (1991) model

(Dechow, Sloan and Sweeney, 1995) as my proxy for discretionary accruals. As mentioned above, I replicate

Tables 8 to 14 using the one-step accruals models recommended by Chen et al. (2018), and find the results

corroborate all inferences drawn from results generated by the two-step model. Along with these two models,

several alternative models exist. I find my results are robust to the use of the original Jones (1991) model; the

modified Jones model (Dechow, Sloan and Sweeney, 1995); the modified Jones model including lead, lagged

and contemporaneous cash flows (Dechow and Dichev, 2002; McNichols, 2002); the performance-adjusted

(Kothari et al., 2005) modified Jones (1991) model (Dechow, Sloan and Sweeney, 1995) with firm and year

fixed effects (Kothari, Mizik and Roychowdhury, 2016), and a model including all parameters listed in the

foregoing models.

4.5.8 Alternative Takeover Prediction Models

I use a takeover prediction model developed by Karpoff et al. (2017) to distinguish between firms with

high and low pre-FINSA likelihood of takeover. This model includes control variables specified in Karpoff et

33

al. (2017) and I include state × year and industry (4-digit SIC) × year fixed effects to control for time varying

state and industry effects which influence takeover activity. I exclude post-treatment (post-2008) years from

the sample generating the takeover prediction score for firms because treatment will endogenously determine

takeover incidence. My results are robust to excluding or altering state, industry and year fixed effects and to

using the full sample of takeover activity instead of the pre-FINSA sample only.

4.6 Limitations

Though my analysis addresses many of the concerns characterizing studies examining firm-level

antitakeover provisions and state-level antitakeover laws, my research design is susceptible to a number of

limitations. First, because I cannot pierce the CFIUS veil, the full population of FINSA-affected firms is not

empirically observable. Consequently, there is noise in my identification of the treatment group and a risk that

treatment firms lie within my control group. This, however, works against my hypothesis because treated firms

in my control group would weaken my DiD estimator. Second, my tests treat frictions imposed on foreign

investment as uniform regardless of the source of the foreign investment. Yet it is intuitive that foreign

investment from countries considered by U.S. regulators to be rivals will face the greatest obstacles.

Consequently, my treatment group is ill-defined to the extent that rival country investments favor sensitive

industries (e.g., industries related to national security) because frictions will be higher for rival country foreign

investment in these industries vis-à-vis rival country foreign investment in less-sensitive industries or by an ally

in sensitive industries. Third, my setting lacks a major advantage of the state-antitakeover law setting:

antitakeover laws are adopted across different states at different times. The source of variation in the takeover

market I use does not vary by geography and time. However, my setting is strong in several other areas where

the state antitakeover law setting is weak. My setting more closely approximates those used in studies examining

the impact of Sarbanes-Oxley Act of 2002 or Regulation FD in that the regulation affects treatment firms at

the same time. Relative to SOX and Reg FD, an advantage of the FINSA setting is that FINSA affects only a

subset of the universe of public firms rather than all public firms. Consequently, I can identify an appropriate

control group that is not affected by the legislation for DiD analysis. Nonetheless, because of the inherent

limitations described above, I suggest caution in interpreting the evidence produced by the FINSA setting.

34

5.0 CONCLUSION

Godsell et al. (2017) document economically significant attenuation in the takeover market for FINSA-

affected firms after FINSA. I exploit this shock to the takeover market to investigate the relation between the

takeover market and earnings management. I discuss the many advantages of this research setting vis-à-vis

previously examined settings used to draw inferences about this relationship. I then use discretionary accruals

to examine earnings management in FINSA-affected firms. I find that FINSA-affected firms boost income by

recording fewer income-decreasing discretionary accruals after FINSA. This result is corroborated by

contemporaneous decreases in income-decreasing special items and write-downs and further corroborated by

tests showing lower financial reporting conservatism for FINSA-affected firms after FINSA. My results are

driven by firms that, pre-FINSA, are more likely to be taken over, consistent with changes in the takeover

market affecting most those firms most susceptible to the takeover market. My findings are robust to a wide

variety of empirical specifications and discretionary accrual measures, as well as the use of firm fixed effects,

and multiple placebo tests. Overall, my results suggest that the takeover market is a managerial disciplining

mechanism that attenuates, rather than accentuates, earnings management.

35

Figure 1: CFIUS notices investigated

This graph shows the percentage of foreign investment notices investigated by the CFIUS. FINSA is enacted in 2008 and fully implemented in 2009.

36

Figure 2: The effect of FINSA on CFIUS articles in the media

0

50

100

150

200

250

300

350

400

450

1989-2005 2006-2016

Unique Articles with "CFIUS" AND "withdraw*"

0

2000

4000

6000

8000

10000

12000

1989-2005 2006-2016

Unique Articles with "CFIUS"

37

Figure 3: The effect of FINSA on CFIUS mentions in SEC filings

38

Figure 4: Pre- vs. Post-FINSA CFIUS process

Pre-FINSA CFIUS Process

Post-FINSA CFIUS Process

Notice of Foreign

Investment

(t = 0)

Review

(30 days)

Investigation

(45 days)

[0–5%]

Recommendation, including informal

mitigation agreements

Presidential Review

(15 days)

No Investigation

[95–100%]

Notice of Foreign

Investment

(t = 0)

Review

(30 days)

Investigation

(45 days)

[40–50%]

Recommendation, including more formal and now monitored

mitigation agreements, breach of which could

force divestment

Presidential Review

(15 days)

No Investigation

[50–60%]

39

Appendix A: Variable Definitions

Variable Name Definition Source

Treatment Industry A firm-level variable equal to one for firms in FINSA-affected industries and zero otherwise.

CFIUS Annual Report

PostFINSA A variable equal to one in years after 2008 and zero otherwise.

Takeover probability The predicted probability of firm takeover (see Cremers et al., 2009; Karpoff et al., 2017; Godsell et al., 2017).

CRSP/Compustat

Accrual Model Variables

Total Accruals Income before extraordinary items reported on the cash flow (IBC) statement minus cash flows from operating activities (OANCF).

CRSP/Compustat

1 / Total Assets 1 divided by the logarithm of total assets (1 / log[lag1AT]) CRSP/Compustat

ΔRevenue − ΔAccounts Receivable

The change in revenue minus the change in accounts receivable. ([(REVT − lag1REVT) − (RECT − lag1RECT)] / lag1AT)

CRSP/Compustat

PPE Property plant and equipment. (PPEGT / lag1AT) CRSP/Compustat

Operating Cash Flows Operating cash flows (OANCF) CRSP/Compustat

Size Logarithm of lagged revenues CRSP/Compustat

Book Leverage Long-term debt plus the current portion of long-term debt divided by lagged total assets. ([DLTT + DLC] / lag1AT)

CRSP/Compustat

Sales Growth The average of sales in t and t -1 divided by sales in t-1 (mean[SALE, lag1SALE] / lag1AT)

CRSP/Compustat

Std(sales) The standard deviation of the logarithm of sales over the past three years, i.e., t, t-1, t-2. (std[log(SALE), log(lag1SALE), log(lag2sale)])

CRSP/Compustat

NOAt-1 Net operating assets, calculated as the sum of shareholders equity and interest-bearing debt, minus cash assets, scaled by sales. ([SEQ + DLTT – CHE] / lag1SALE)

CRSP/Compustat

Market Return Annual closing price minus lagged annual closing price divided by lagged annual closing price ([PRCC_F – lag1PRCC_F] / lag1PRCC_F)

CRSP/Compustat

Takeover Model Variables

Market-to-Book Ratio Annual closing price × common shares outstanding divided by shareholders equity (or alternate variable) plus deferred taxes and investment tax credit minus preferred stock. ([PRCC_F × CSHO] / [(COALESCE[SEQ,CEQ+UPSTK,AT – LT] + TXDITC − COALESCE [PSTKRV, PSTKL, UPSTK])])

CRSP/Compustat

Property Ratio Property, plant and equipment (gross) divided by lagged assets. (PPEGT/lag1AT)

CRSP/Compustat

Liquidity Ratio Current assets minus current liabilities divided by lagged total assets. ([ACT – LCT] / lag1AT)

CRSP/Compustat

Return on Assets (ROA)

Income before extraordinary items divided by lagged total assets. (IB / lag1AT)

CRSP/Compustat

Herfindahl-Hirschman Index

Sales divided by 2-digit SIC industry sales squared. CRSP/Compustat

40

Foreign Sales Pretax Foreign Income. (PIFO / lag1AT) CRSP/Compustat

Number of Analysts Following

The number of analysts following the firm. IBES

Institutional Ownership

The proportion of shares held by institutional investors. The partition is split at median institutional ownership. Institutional ownership is redefined as 100% for values of institutional ownership greater than 100%.

Thomson Reuters

Change in Employees Number of employees minus lagged number of employees divided by lagged number of employees. ([EMP − lag1EMP] / lag1EMP)

CRSP/Compustat

Economic Volatility and Idiosyncratic Risk Variables

Loss Indicator A variable equal to one if firm earnings before extraordinary items (IB) are below zero, and zero otherwise.

CRSP/Compustat

Operating Cycle Natural log of the firm’s operating cycle measured in days, based on turnover in accounts receivable and receivable and inventory. Specifically, the firm’s operating cycle is calculated as: [180 × (RECTt + RECTt-1) ÷ SALE] + [(INVt + INVt-1) ÷ COGS].

CRSP/Compustat

Stock Volatility Return volatility measured as the standard deviation of annual stock returns over the prior three fiscal years.

CRSP/Compustat

Standard Deviation of Earnings

Standard deviation of annual earnings before extraordinary items (IB) deflated by lagged total assets over the past three years.

CRSP/Compustat

Standard Deviation of Cash Flows

Standard deviation of annual cash flows deflated by lagged total assets over the past three years.

CRSP/Compustat

Industry Adjusted Cash Flows

Annual cash flows from operations minus the median cash flows from operations for all firms in the same industry (based on 2-digit SIC code) in the same quarter.

CRSP/Compustat

Below-Industry Cash Flow Indicator

An indicator variable equal to one if Industry Adjusted Cash Flows are below zero, and zero otherwise.

CRSP/Compustat

Conservatism Model Variables

Net Income Income before extraordinary items (IB) divided by market value (MKVALT)

CRSP/Compustat

Market Value Market value (MKVALT) CRSP/Compustat

RET RET is the buy-and-hold stock return of firm i over year t. CRSP/Compustat

NEG NEG is an indicator variable equal to one if RET is negative, and zero otherwise.

CRSP/Compustat

41

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45

(1) (2) (3) (4)

CFIUS Number

of Notices and

Reviews

Hand

Collection

Number of

Notices and

Reviews

CFIUS

Number of

Investigations

Hand

Collection

Number of

Investigations

1995 81 4 0 0

1996 55 3 0 0

1997 62 15 0 0

1998 65 16 2 0

1999 79 31 0 1

2000 73 32 1 3

2001 55 10 1 0

2002 43 2 0 0

2003 41 6 2 1

2004 53 10 2 0

2005 65 27 1 1

2006 111 39 7 2

2007 138 52 6 0

2008 155 64 23 0

2009 65 17 25 0

2010 93 32 35 1

2011 111 35 40 1

2012 114 41 45 4

2013 97 30 48 2

2014 147 51 51 5

2015 143 64 66 6

2016 ND 83 ND 2

Total 1,846 664 355 29

ND = Not Disclosed

Table 1: Comparison of CFIUS Reported Activity and CFIUS Activity

Captured in Hand Collected Data

This table summarizes data generated by the hand-collection process used to

identify firms scrutinized by CFIUS.

46

CRSP Compustat firm-years 1989-2015 120,738

Less: Firm-years in industries with 2-digit SIC values between 60 and 69 (inclusive) (4,201) 116,537

Less: Firm-years with missing test and control variables (54,279) 62,258

Less: Firm-years with fewer than 20 firm-years in its SIC2-year cluster (5,311) 56,947

Less: Firm-years without pre-FINSA (2007) takeover prediction score (24,207) 32,740

Firm-years in panel used to test H1: 32,740

FINSA (Treatment) Firm-Years:

Non-FINSA (Control) Firm-Years:

Total Firms:

Firm-years in low takeover probability sample:

Firm-years in high takeover probability sample:

Total Firms: FINSA

(Treatment) Firm-

Years

Non-FINSA (Control)

Firm-Years

Firm-years in low takeover probability sample: 5,330 11,046

Firm-years in high takeover probability sample: 5,608 10,756

Table 2: Sample Construction

This table reports the construction of the estimation panel.

Firm-Years

16,376

32,740

Firm-Years

10,938

32,740

21,802

16,364

47

Fama-French industry code 10 industries) Frequency % Frequency %

Non-Durable 0 0.0% 2,118 9.7%

Consumer Durable 0 0.0% 978 4.5%

Manufacturing 454 4.2% 5,443 25.0%

Energy 1,478 13.5% 551 2.5%

High Tech 5,478 50.1% 2,636 12.1%

Telecommunication 831 7.6% 518 2.4%

Wholesale Retail 0 0.0% 2,878 13.2%

Health 1,303 11.9% 3,511 16.1%

Utilities 1,190 10.9% 760 3.5%

Other (Mines, Construction) 204 1.9% 2,409 11.0%

Total 10,938 21,802

Year Freq. % Freq. %

1991 216 2.0% 514 2.4%

1992 228 2.1% 536 2.5%

1993 233 2.1% 545 2.5%

1994 246 2.2% 579 2.7%

1995 263 2.4% 626 2.9%

1996 295 2.7% 700 3.2%

1997 315 2.9% 753 3.5%

1998 352 3.2% 800 3.7%

1999 386 3.5% 856 3.9%

2000 434 4.0% 912 4.2%

2001 468 4.3% 957 4.4%

2002 547 5.0% 1,025 4.7%

2003 625 5.7% 1,116 5.1%

2004 643 5.9% 1,184 5.4%

2005 686 6.3% 1,254 5.8%

2006 730 6.7% 1,316 6.0%

2007 804 7.4% 1,454 6.7%

2008 718 6.6% 1,303 6.0%

2009 643 5.9% 1,224 5.6%

2010 588 5.4% 1,150 5.3%

2011 545 5.0% 1,070 4.9%

2012 509 4.7% 986 4.5%

2013 464 4.2% 942 4.3%

Total 10,938 21,802

FINSA (Treatment)

Firm-Years

Table 3: Sample Breakdown by Industry and Year

This table reports sample characteristics of the control and treated firms.

Panel A: Industry Breakdown

FINSA (Treatment)

Firm-Years

Panel B: Year Breakdown

Non-FINSA (Control)

Firm-Years

Non-FINSA (Control)

Firm-Years

48

Variable Mean Median St. Dev. N Mean Median St. Dev. N

Total Accruals -0.054 -0.049 0.094 21802 -0.079 -0.065 0.112 10938

Discretationary Accruals 0.003 0.005 0.087 21802 -0.001 0.003 0.100 10938

Abs(Disc. Accruals) 0.058 0.038 0.065 21802 0.067 0.042 0.075 10938

Positive Disc. Accruals 0.057 0.039 0.060 11659 0.063 0.040 0.069 5716

Negative Disc. Accruals 0.060 0.037 0.072 10143 0.072 0.044 0.081 5222

Post-FINSA 0.246 0.000 0.431 21802 0.251 0.000 0.434 10938

1 / Total Assets 0.013 0.002 0.030 21802 0.014 0.002 0.030 10938

ΔRevenue - ΔAccounts Receivable 0.063 0.055 0.206 21802 0.046 0.040 0.199 10938

Property, Plant and Equipment, Gross 0.541 0.470 0.356 21802 0.610 0.456 0.482 10938

Cash Flowst-1 0.068 0.087 0.141 21802 0.064 0.087 0.160 10938

Cash Flowst 0.069 0.087 0.139 21802 0.065 0.087 0.158 10938

Cash Flowst+1 0.069 0.087 0.139 21802 0.066 0.088 0.158 10938

Size 5.982 5.921 2.054 21802 6.280 6.030 2.429 10938

Book Leverage 0.207 0.185 0.184 21802 0.191 0.149 0.192 10938

Sales Growth 0.062 0.038 0.171 21802 0.080 0.045 0.205 10938

Std(Sales) 0.130 0.087 0.135 21802 0.123 0.078 0.136 10938

NOAt-1 0.269 0.115 0.505 21802 0.397 0.113 0.647 10938

Market Return 0.216 0.102 0.789 21802 0.243 0.086 0.951 10938

Market-to-Book Ratio 2.674 1.900 3.330 21802 2.865 1.907 3.708 10938

Property Ratio 0.277 0.220 0.216 21802 0.308 0.194 0.275 10938

Liqudity Ratio 0.266 0.242 0.225 21802 0.258 0.230 0.254 10938

Change in Employees 0.067 0.027 0.248 21802 0.077 0.029 0.267 10938

Return on Assets 0.061 0.085 0.160 21802 0.024 0.062 0.184 10938

HHI 0.001 0.000 0.009 21802 0.001 0.000 0.004 10938

Foreign Sales 0.013 0.000 0.035 21802 0.015 0.000 0.047 10938

Number of Analysts Following 5.124 3.000 5.795 21802 5.834 3.000 6.750 10938

Institutional Ownership 0.476 0.499 0.311 21802 0.434 0.430 0.316 10938

Special Items -0.012 0.000 0.044 21802 -0.014 0.000 0.049 10938

Writedowns -0.001 0.000 0.005 21802 -0.001 0.000 0.006 10938

Cash Flowst-2 0.065 0.087 0.145 21802 0.060 0.086 0.165 10938

Cash Flowst+2 0.068 0.088 0.141 21802 0.065 0.087 0.158 10938

Loss Indicator 0.223 0.000 0.416 21802 0.304 0.000 0.460 10938

Operating Cycle 4.234 2.908 21.033 21802 3.839 3.218 23.482 10938

Stock Volatility 0.578 0.355 0.835 21802 0.709 0.414 1.002 10938

Standard Deviation of Earnings 0.056 0.024 0.097 21802 0.082 0.037 0.124 10938

Standard Deviation of Cash Flows 0.055 0.036 0.064 21802 0.063 0.040 0.073 10938

Industry Adjusted Cash Flows 0.007 0.021 0.133 21802 0.003 0.019 0.152 10938

Below-Industry Cash Flow Indicator 0.384 0.000 0.486 21802 0.391 0.000 0.488 10938

Table 4: Sample Summary Statistics

This table reports summary statistics for the sample. All variables defined in Appendix A.

Non-FINSA (Control) Firms FINSA (Treatment) Firms

49

(1) (2)

Total Accruals

Performance-Adjusted (Kothari et

al, 2005) Modified Jones (1991)

Discretionary Accruals

Treatment Industry × Post-FINSA 0.00418* 0.00434*

(1.86) (1.87)

1 / Total Assets -0.0344

(-0.78)

ΔRevenue - ΔAccounts Receivable 0.00406

(1.00)

Property, Plant and Equipment, Gross 0.000518

(0.10)

Cash Flowst -1 0.0212** 0.0280***

(2.28) (3.08)

Cash Flowst -0.890*** -0.877***

(-60.51) (-59.56)

Cash Flowst +1 0.0105 0.0144

(1.14) (1.56)

Size -0.000862 -0.00333**

(-0.56) (-2.38)

Book Leverage -0.0600*** -0.0579***

(-9.39) (-8.92)

Sales Growth -0.0185*** -0.0249***

(-3.01) (-4.75)

Std(Sales) -0.0145** -0.0157***

(-2.50) (-2.72)

NOAt -1 0.00636** 0.00594**

(2.54) (2.34)

Market Return 0.00146* 0.00152*

(1.75) (1.83)

Market-to-Book Ratio -0.000466* -0.000498*

(-1.75) (-1.86)

Property Ratio -0.0382*** 0.0386***

(-3.70) (4.32)

Liqudiity Ratio 0.0459*** 0.0459***

(7.06) (7.03)

Change in Employees -0.0164*** -0.0205***

(-5.33) (-6.67)

Return on Assets 0.774*** 0.567***

(54.13) (40.07)

HHI -0.0129 -0.0196

(-0.32) (-0.39)

Foreign Sales 0.268*** 0.255***

(10.48) (9.94)

Number of Analysts Following -0.000256 -0.000515***

(-1.46) (-2.89)

Institutional Ownership -0.00297 -0.00345

(-0.94) (-1.07)

Number of Observations 32740 32740

Adjusted R-Squared 0.597 0.511

Standard Errors Clustered By: Firm Firm

Firm Fixed Effects: Yes Yes

Year Fixed Effects: Yes Yes

Table 5: Earnings Management for FINSA-Affected Firms After FINSA

This table reports results from regressions of accruals on firm-level characteristics including an interactive indicator variable

capturing the years following FINSA and FINSA-affected firms. Post-FINSA is a variable equal to one in the years after

2008 and zero otherwise. Treatment Industry is a variable equal to one when the firm is in a FINSA-affected industry and

zero otherwise. Post-FINSA is subsumed by year fixed effects. Treatment Industry is subsumed by firm fixed effects. The

dependent variable in columns 1 (2) is total accruals (performance-adjusted (Kothari et al., 2005) modified Jones (Dechow,

Sweeney and Sloan, 1995) discretionary accruals). Other variables are defined in Appendix A. Standard errors are clustered

by firm. T-statistics are presented underneath the coefficient estimates. ***, **, and * denote two-tailed significance levels at

1%, 5%, and 10%, respectively.

50

(1) (2) (3) (4)

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Treatment Industry × Post-FINSA -0.000811 0.00876*** 0.000163 0.00860***

(-0.23) (3.08) (0.04) (2.93)

1 / Total Assets -0.0506 0.00834

(-1.03) (0.04)

ΔRevenue - ΔAccounts Receivable 0.000373 0.0138**

(0.07) (2.37)

Property, Plant and Equipment, Gross 0.0100 -0.00992

(1.39) (-1.36)

Cash Flowst -1 0.0231** 0.0119 0.0310*** 0.0136

(2.11) (0.70) (2.89) (0.82)

Cash Flowst -0.872*** -0.945*** -0.858*** -0.934***

(-48.65) (-40.05) (-47.91) (-39.78)

Cash Flowst +1 0.00197 0.0404** 0.00547 0.0449**

(0.19) (2.26) (0.51) (2.51)

Size 0.000960 -0.00308 -0.000813 -0.00587***

(0.40) (-1.44) (-0.40) (-3.01)

Book Leverage -0.0677*** -0.0547*** -0.0654*** -0.0518***

(-6.67) (-7.02) (-6.32) (-6.55)

Sales Growth -0.0107 -0.0370*** -0.0183*** -0.0386***

(-1.39) (-3.90) (-2.76) (-4.65)

Std(Sales) -0.00502 -0.0297*** -0.00498 -0.0319***

(-0.61) (-4.04) (-0.61) (-4.26)

NOAt -1 0.00625* 0.00557 0.00573 0.00517

(1.77) (1.63) (1.60) (1.49)

Market Return 0.000622 0.00336** 0.000692 0.00344**

(0.59) (2.31) (0.66) (2.40)

Market-to-Book Ratio -0.000522 -0.000384 -0.000558 -0.000397

(-1.24) (-1.24) (-1.32) (-1.29)

Property Ratio -0.0279* -0.0448*** 0.0537*** 0.0221**

(-1.78) (-3.77) (3.84) (2.07)

Liqudiity Ratio 0.0571*** 0.0241** 0.0566*** 0.0238**

(6.80) (2.27) (6.64) (2.26)

Change in Employees -0.0144*** -0.0179*** -0.0187*** -0.0216***

(-3.43) (-4.08) (-4.44) (-4.93)

Return on Assets 0.772*** 0.777*** 0.562*** 0.575***

(44.33) (29.56) (32.89) (22.04)

HHI -16.76 -0.0219 -16.39 -0.0227

(-1.44) (-0.60) (-1.28) (-0.55)

Foreign Sales 0.290*** 0.254*** 0.278*** 0.247***

(7.62) (7.60) (7.23) (7.46)

Number of Analysts Following -0.000107 -0.000300 -0.000565 -0.000406**

(-0.30) (-1.52) (-1.55) (-2.03)

Institutional Ownership -0.00973* 0.00539 -0.0128** 0.00594

(-1.95) (1.37) (-2.51) (1.47)

Number of Observations 16376 16364 16376 16364

Adjusted R-Squared 0.597 0.601 0.509 0.516

Standard Errors Clustered By: Firm Firm Firm Firm

Firm Fixed Effects: Yes Yes Yes Yes

Year Fixed Effects: Yes Yes Yes Yes

Table 6: Earnings Management for FINSA-Affected Firms After FINSA, Partitioned on Takeover Probability

This table reports results from regressions of accruals on firm-level characteristics including an interactive indicator variable capturing the years

following FINSA and FINSA-affected firms. Post-FINSA is a variable equal to one in the years after 2008 and zero otherwise. Treatment Industry is

a variable equal to one when the firm is in a FINSA-affected industry and zero otherwise. Post-FINSA and Treatment Industry are subsumed by year

and firm fixed effects. The dependent variable in columns 1 and 2 (3 and 4) is total accruals (performance-adjusted (Kothari et al., 2005) modified

Jones (Dechow, Sweeney and Sloan, 1995) discretionary accruals). Samples examined in odd (even) columns are low (high) takeover probability firms.

Other variables are defined in Appendix A. Standard errors are clustered by firm. T-statistics are presented underneath the coefficient estimates. ***,

**, and * denote two-tailed significance levels at 1%, 5%, and 10%, respectively.

Total AccrualsPerformance-Adjusted (Kothari et al., 2005)

Modified Jones (1991) Discretionary Accruals

51

(1) (2) (3) (4) (5) (6)

All firmsLow Takeover

Probability

High Takeover

ProbabilityAll firms

Low Takeover

Probability

High Takeover

Probability

Treatment Industry × Post-Placebo 0.00351 0.00413 0.00240 0.00273 0.00359 0.00178

(1.30) (0.98) (0.74) (1.00) (0.85) (0.53)

1 / Total Assets -0.0153 -0.0294 0.213

(-0.31) (-0.51) (0.94)

ΔRevenue - ΔAccounts Receivable 0.000192 -0.00511 0.0148**

(0.04) (-0.82) (2.16)

Property, Plant and Equipment, Gross 0.00578 0.0123 -0.00258

(0.87) (1.27) (-0.29)

Cash Flowst -1 0.0178* 0.0243* -0.00473 0.0254** 0.0334*** -0.00437

(1.67) (1.91) (-0.27) (2.44) (2.68) (-0.26)

Cash Flowst -0.900*** -0.870*** -0.985*** -0.886*** -0.856*** -0.973***

(-51.54) (-40.08) (-41.26) (-50.59) (-39.27) (-41.21)

Cash Flowst +1 0.00944 0.00111 0.0394** 0.0135 0.00417 0.0435**

(0.92) (0.09) (2.10) (1.31) (0.35) (2.28)

Size -0.00176 -0.00158 -0.00219 -0.00440** -0.00322 -0.00586***

(-0.94) (-0.49) (-0.94) (-2.56) (-1.19) (-2.69)

Book Leverage -0.0592*** -0.0658*** -0.0539*** -0.0579*** -0.0641*** -0.0535***

(-7.83) (-5.51) (-5.89) (-7.55) (-5.28) (-5.79)

Sales Growth -0.0154** -0.00324 -0.0437*** -0.0241*** -0.0133* -0.0456***

(-2.35) (-0.41) (-4.45) (-4.29) (-1.91) (-5.44)

Std(Sales) -0.0252*** -0.0129 -0.0440*** -0.0272*** -0.0134 -0.0467***

(-3.88) (-1.36) (-5.70) (-4.20) (-1.43) (-5.96)

NOAt -1 0.00932*** 0.00695* 0.0108*** 0.00930*** 0.00653 0.0111***

(3.29) (1.70) (3.15) (3.25) (1.58) (3.21)

Market Return 0.00138 0.000798 0.00285 0.00139 0.000831 0.00289

(1.45) (0.70) (1.56) (1.47) (0.73) (1.61)

Market-to-Book Ratio -0.000244 -0.000156 -0.000356 -0.000293 -0.000224 -0.000350

(-0.78) (-0.32) (-1.04) (-0.92) (-0.45) (-1.04)

Property Ratio -0.0417*** -0.0269 -0.0465*** 0.0430*** 0.0579*** 0.0318***

(-3.22) (-1.40) (-3.05) (4.50) (3.85) (2.70)

Liqudiity Ratio 0.0557*** 0.0692*** 0.0322*** 0.0558*** 0.0683*** 0.0315***

(7.75) (7.19) (2.94) (7.64) (6.94) (2.81)

Change in Employees -0.0152*** -0.0152*** -0.0130*** -0.0192*** -0.0194*** -0.0167***

(-4.58) (-3.17) (-3.06) (-5.76) (-4.04) (-3.84)

Return on Assets 0.783*** 0.772*** 0.803*** 0.574*** 0.561*** 0.599***

(45.59) (36.21) (29.01) (33.65) (26.75) (21.71)

HHI -0.0112 -21.61 -0.0274 -0.0193 -21.69 -0.0264

(-0.28) (-1.43) (-0.68) (-0.37) (-1.32) (-0.57)

Foreign Sales 0.300*** 0.326*** 0.289*** 0.285*** 0.316*** 0.279***

(9.44) (6.90) (6.99) (8.93) (6.65) (6.74)

Number of Analysts Following -0.0000726 0.000414 -0.000227 -0.000344 -0.000133 -0.000313

(-0.32) (0.89) (-0.91) (-1.51) (-0.29) (-1.24)

Institutional Ownership 0.00255 -0.00751 0.0121* 0.00129 -0.0115 0.0121*

(0.54) (-1.03) (1.88) (0.27) (-1.55) (1.88)

Number of Observations 24619 12210 12409 24619 12210 12409

Adjusted R-Squared 0.613 0.608 0.627 0.530 0.522 0.549

Standard Errors Clustered By: Firm Firm Firm Firm Firm Firm

Firm Fixed Effects: Yes Yes Yes Yes Yes Yes

Year Fixed Effects: Yes Yes Yes Yes Yes Yes

Table 7: Parallel Trends Assumption Validity Test - Placebo DiD

This table reports results from regressions of accruals on firm-level characteristics including an interactive indicator variable capturing the years following a placebo event year (2002) and FINSA-affected

firms. Years after 2008 are excluded from the sample. Post-Placebo is a variable equal to one in the years after 2005 and zero otherwise. Treatment Industry is a variable equal to one when the firm is in

a FINSA-affected industry and zero otherwise. Post-FINSA and Treatment Industry are subsumed by year and firm fixed effects. The dependent variable in columns 1 and 2 (3 and 4) is total accruals

(performance-adjusted (Kothari et al., 2005) modified Jones (Dechow, Sweeney and Sloan, 1995) discretionary accruals). Samples examined in odd (even) columns are low (high) takeover probability

firms. Other variables are defined in Appendix A. Standard errors are clustered by firm. T-statistics are presented underneath the coefficient estimates. ***, **, and * denote two-tailed significance levels

at 1%, 5%, and 10%, respectively.

Total AccrualsPerformance-Adjusted (Kothari et al., 2005) Modified Jones (1991)


52

(1) (2) (3) (4) (5) (6) (7) (8)

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Treatment Industry × Post-FINSA 0.000163 0.00860*** -0.0000363 -0.00526** -0.00345 0.00109 -0.00516 -0.0145***

(0.04) (2.93) (-0.01) (-2.48) (-0.96) (0.44) (-1.09) (-4.16)

Cash Flowst -1 0.0310*** 0.0136 -0.0226*** -0.0104 0.0103 -0.00224 -0.0283** -0.0128

(2.89) (0.82) (-2.62) (-0.79) (1.02) (-0.12) (-2.02) (-0.80)

Cash Flowst -0.858*** -0.934*** -0.0190 0.00627 -0.596*** -0.676*** 0.432*** 0.520***

(-47.91) (-39.78) (-1.41) (0.33) (-23.12) (-18.14) (18.31) (16.13)

Cash Flowst +1 0.00547 0.0449** -0.00769 0.00911 0.000308 0.0277** -0.0322** -0.0485***

(0.51) (2.51) (-0.95) (0.80) (0.03) (2.10) (-2.36) (-2.69)

Size -0.000813 -0.00587*** -0.0143*** -0.00952*** -0.0152*** -0.0135*** -0.00728*** -0.00450**

(-0.40) (-3.01) (-8.01) (-6.54) (-8.37) (-8.16) (-2.72) (-1.97)

Book Leverage -0.0654*** -0.0518*** 0.0203*** 0.0107 -0.0298*** -0.0304*** 0.0449*** 0.0333***

(-6.32) (-6.55) (2.69) (1.51) (-3.32) (-4.41) (3.87) (3.13)

Sales Growth -0.0183*** -0.0386*** 0.0295*** 0.0367*** 0.00974** 0.00408 0.0332*** 0.0498***

(-2.76) (-4.65) (6.35) (5.80) (2.02) (0.62) (3.81) (5.19)

Std(Sales) -0.00498 -0.0319*** 0.0736*** 0.0627*** 0.0527*** 0.0272*** 0.0650*** 0.0687***

(-0.61) (-4.26) (11.95) (9.05) (7.46) (4.23) (6.63) (7.42)

NOAt -1 0.00573 0.00517 -0.00344 -0.00747** 0.000901 -0.000808 -0.00788* -0.00612

(1.60) (1.49) (-1.34) (-2.57) (0.27) (-0.30) (-1.74) (-1.19)

Market Return 0.000692 0.00344** 0.00244*** 0.000505 0.00313*** 0.00164 0.00115 -0.00107

(0.66) (2.40) (2.77) (0.56) (3.67) (1.45) (0.87) (-0.69)

Market-to-Book Ratio -0.000558 -0.000397 0.000726** 0.000403* -0.000131 0.000218 0.00117*** 0.000455

(-1.32) (-1.29) (2.19) (1.69) (-0.32) (0.65) (2.73) (1.32)

Property Ratio 0.0537*** 0.0221** -0.0426*** -0.0210** -0.0147 -0.00282 -0.0688*** -0.0344***

(3.84) (2.07) (-4.32) (-2.55) (-1.24) (-0.33) (-4.71) (-2.78)

Liqudiity Ratio 0.0566*** 0.0238** -0.0110* 0.00353 0.00567 0.0104 -0.0445*** -0.0114

(6.64) (2.26) (-1.70) (0.48) (0.73) (1.27) (-4.73) (-1.04)

Change in Employees -0.0187*** -0.0216*** 0.0143*** 0.0112*** 0.00553 0.00485 0.0326*** 0.0260***

(-4.44) (-4.93) (4.46) (3.26) (1.54) (1.19) (6.00) (5.18)

Return on Assets 0.562*** 0.575*** -0.00624 -0.0661*** 0.407*** 0.382*** -0.298*** -0.348***

(32.89) (22.04) (-0.55) (-3.72) (20.92) (14.37) (-15.56) (-13.68)

HHI -16.39 -0.0227 13.53 0.0433** 0.558 0.0323 16.16 0.443**

(-1.28) (-0.55) (1.25) (2.09) (0.08) (1.47) (1.17) (2.10)

Foreign Sales 0.278*** 0.247*** -0.0989*** -0.142*** 0.0506* 0.0695*** -0.311*** -0.271***

(7.23) (7.46) (-3.65) (-6.16) (1.79) (2.74) (-7.46) (-7.83)

Number of Analysts Following -0.000565 -0.000406** 0.00106*** 0.000454*** 0.000585* 0.000148 0.000645 0.000568**

(-1.55) (-2.03) (3.19) (2.74) (1.73) (0.87) (1.43) (2.22)

Institutional Ownership -0.0128** 0.00594 0.00224 -0.00362 -0.000818 0.00802** 0.00465 -0.00332

(-2.51) (1.47) (0.50) (-0.97) (-0.16) (2.37) (0.73) (-0.60)

Number of Observations 16376 16364 16376 16364 8951 8424 7425 7940

Adjusted R-Squared 0.509 0.516 0.236 0.274 0.496 0.549 0.388 0.428

Standard Errors Clustered By: Firm Firm Firm Firm Firm Firm Firm Firm

Firm Fixed Effects: Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed Effects: Yes Yes Yes Yes Yes Yes Yes Yes

Table 8: Decomposed Discretionary Accruals, Partitioned on Takeover Probability

This table reports results from regressions of accruals on firm-level characteristics including an interactive indicator variable capturing the years following FINSA and FINSA-affected firms. Post-FINSA is a variable equal to one in the years after 2008

and zero otherwise. Treatment Industry is a variable equal to one when the firm is in a FINSA-affected industry and zero otherwise. Post-FINSA and Treatment Industry are subsumed by year and firm fixed effects. The dependent variable in columns

1 and 2 is performance-adjusted (Kothari et al., 2005) modified Jones (Dechow, Sweeney and Sloan, 1995) discretionary accruals. The dependent variable in columns 3 and 4 (5 and 6) [7 and 8] is the absolute value of all (positive) [negative] discretionary

accruals. Samples examined in odd (even) columns are low (high) takeover probability firms. Other variables are defined in Appendix A. Standard errors are clustered by firm. T-statistics are presented underneath the coefficient estimates. ***, **, and *

denote two-tailed significance levels at 1%, 5%, and 10%, respectively.

Absolute Value of Performance-Adjusted

(Kothari et al, 2005) Modified Jones (1991)


Negative Performance-Adjusted (Kothari et al,

2005) Modified Jones (1991) Discretionary

Accruals

Performance-Adjusted (Kothari et al, 2005)


Positive Performance-Adjusted (Kothari et al,

2005) Modified Jones (1991) Discretionary

Accruals

53

(1) (2) (3) (4) (5) (2) (7) (8)

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Treatment Industry × Post-FINSA 0.000968 0.00569*** -0.00673 -0.00262 -0.00104 -0.00482** -0.000127 -0.000488**

(0.26) (2.64) (-1.42) (-0.85) (-0.25) (-2.04) (-0.50) (-2.38)

Cash Flowst 0.0247* -0.00437 0.0175 -0.0388** 0.00152 0.0106 0.00128* 0.000137

(1.75) (-0.31) (1.47) (-2.40) (0.10) (0.67) (1.91) (0.13)

Size -0.00947*** -0.00487*** -0.00404* -0.00278* 0.00506** 0.00385** 0.000254** 0.000523***

(-4.28) (-2.62) (-1.79) (-1.69) (2.01) (1.97) (2.26) (5.02)

Book Leverage -0.0242** -0.0304*** 0.000829 -0.0161** 0.0221** 0.0322*** 0.00106* 0.00196***

(-2.40) (-3.94) (0.08) (-1.99) (2.05) (3.70) (1.89) (3.16)

Sales Growth -0.0157** -0.00927 -0.000967 0.00662 0.0110 0.0114* 0.000376 0.000138

(-2.38) (-1.34) (-0.18) (1.11) (1.53) (1.67) (1.19) (0.34)

Std(Sales) -0.0474*** -0.0438*** 0.0238*** 0.00574 0.0622*** 0.0483*** 0.000641 0.00136***

(-5.41) (-6.12) (2.88) (0.80) (6.49) (5.41) (1.52) (2.84)

NOAt -1 0.00258 0.0115*** -0.000140 0.00265 -0.000550 -0.0122*** -0.000223 -0.000186

(0.66) (3.32) (-0.05) (1.11) (-0.13) (-3.37) (-1.27) (-1.02)

Market Return 0.00466*** 0.00554*** -0.0000203 0.000646 -0.00300* -0.00429** -0.000134* -0.000278***

(3.23) (3.03) (-0.02) (0.60) (-1.79) (-2.11) (-1.90) (-4.75)

Market-to-Book Ratio -0.000469 -0.000530** 0.000408 -0.0000228 0.000679 0.000732*** -0.0000491** 0.0000116

(-1.30) (-2.05) (1.15) (-0.10) (1.65) (2.97) (-2.21) (0.57)

Property Ratio 0.0158 -0.0357*** -0.0127 -0.0538*** -0.00942 0.0249** -0.000943 -0.000472

(1.26) (-3.57) (-0.98) (-5.10) (-0.56) (2.11) (-1.15) (-0.69)

Liqudiity Ratio 0.0320*** 0.00908 0.0282*** 0.00393 -0.0160* 0.00385 -0.000781 0.0000357

(3.69) (1.12) (3.58) (0.44) (-1.70) (0.42) (-1.55) (0.06)

Change in Employees -0.00837** -0.00758** 0.00220 0.000963 0.0109** 0.00877** -0.000757*** -0.000685***

(-2.08) (-2.04) (0.59) (0.24) (2.57) (2.19) (-3.74) (-3.02)

Return on Assets 0.0464*** 0.0712*** -0.0456*** -0.0165 -0.0760*** -0.0860*** -0.00459*** -0.00328***

(3.52) (4.26) (-3.31) (-0.82) (-5.16) (-4.50) (-5.96) (-2.98)

HHI -7.119* -0.174 -37.22** -0.145 6.585* 0.146 0.217 0.000406

(-1.70) (-1.19) (-2.18) (-1.26) (1.74) (0.88) (0.89) (0.15)

Foreign Sales 0.315*** 0.310*** 0.0576 0.0158 -0.274*** -0.339*** -0.0116*** -0.0144***

(9.54) (11.80) (1.38) (0.48) (-7.77) (-11.32) (-4.53) (-6.16)

Number of Analysts Following 0.000252 0.000155 -0.000345 -0.000217 -0.000349 -0.000227 0.0000305 -0.0000333**

(0.73) (0.78) (-0.84) (-1.00) (-0.89) (-1.03) (1.26) (-2.23)

Institutional Ownership 0.0115** 0.00759* -0.00464 -0.00280 -0.0159*** -0.00663 -0.000819** -0.000374

(2.07) (1.91) (-0.73) (-0.62) (-2.70) (-1.50) (-2.30) (-1.34)



Standard Errors Clustered By: SIC4 SIC4 SIC4 SIC4 SIC4 SIC4 SIC4 SIC4

Industry (SIC4) Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes


Table 9: Model-Free Measures of Earnings Management

This table reports results from regressions of model independent indicators of earnings management on firm-level characteristics including an interactive indicator variable capturing the years following FINSA and FINSA-affected firms. Main effects

subsumed by firm and year fixed effects are Post-FINSA, a variable equal to one in the years after 2008 and zero otherwise, and Treatment Industry, a variable equal to one when the firm is in a FINSA-affected industry and zero otherwise. The

dependent variable in column 1 and 2 (3 and 4) is special items (writedowns). Samples examined in odd (even) columns are low (high) takeover probability firms. Other variables are defined in Appendix A. Firm and year fixed effects are included, and

standard errors are clustered by firm. T-statistics are presented underneath the coefficient estimates. ***, **, and * denote two-tailed significance levels at 1%, 5%, and 10%, respectively.

WritedownsSpecial Items Positive Special Items Negative Special Items

54

(1) (3) (3)

Net Income Net Income Net Income

All FirmsLow Takeover

Probability

High Takeover

Probability

RET 0.00723 0.00388 0.0120

(0.99) (0.51) (0.66)

NEG 0.0826*** 0.0595*** 0.120***

(3.96) (3.14) (2.80)

RET × NEG 0.402*** 0.357*** 0.495***

(4.67) (3.96) (2.90)

Post-FINSA × NEG -0.0428** -0.0566*** -0.0149

(-2.25) (-3.05) (-0.44)

Post-FINSA × RET 0.0109 0.0106 0.0133

(0.83) (1.28) (0.39)

Post-FINSA × RET × NEG -0.00652 -0.118 0.204

(-0.06) (-1.25) (0.86)

Treatment Industry × Post-FINSA -0.00971 0.0101 -0.0268

(-0.80) (0.73) (-1.30)

Treatment Industry × NEG 0.0754*** 0.0514 0.105**

(2.92) (1.65) (2.44)

Treatment Industry × RET 0.00146 0.00382 -0.000273

(0.23) (0.49) (-0.02)

Treatment Industry × RET × NEG 0.337*** 0.236* 0.465***

(3.15) (1.86) (2.61)

Treatment Industry × Post-FINSA × NEG -0.0745** -0.0338 -0.130**

(-2.06) (-0.67) (-2.50)

Treatment Industry × Post-FINSA × RET 0.0271 -0.00176 0.0590

(1.29) (-0.14) (1.29)

Treatment Industry × Post-FINSA × RET × NEG -0.346* -0.0275 -0.803**

(-1.76) (-0.12) (-2.40)

Stock Volatility 0.0251*** 0.0175*** 0.0360***

(4.92) (3.92) (2.97)

Market Value -0.000000956*** -0.000000405 -0.000000883**

(-3.27) (-0.29) (-2.55)

Market-to-Book Ratio -0.00112 -0.00147 -0.00121

(-1.47) (-1.27) (-1.18)

Book Leverage -0.255*** -0.239*** -0.242***

(-7.81) (-4.88) (-5.33)

Stock Volatility × RET -0.00648 -0.0103** 0.00739

(-1.43) (-2.12) (0.79)

Stock Volatility × NEG -0.0174** 0.0000683 -0.0461***

(-2.14) (0.01) (-3.21)

Stock Volatility × RET × NEG -0.0605** -0.00109 -0.189***

(-1.99) (-0.03) (-3.86)

Market Value × RET -0.00000121*** -0.000000355 -0.00000133***

(-3.92) (-0.43) (-2.83)

Market Value × NEG -0.00000245*** -0.00000912** -0.00000221***

(-5.16) (-2.03) (-3.67)

Market Value × RET × NEG -0.0000122*** -0.0000632*** -0.00000952***

(-4.97) (-2.63) (-3.20)

Market-to-Book Ratio × RET 0.000750 0.000943* 0.000638

(1.35) (1.80) (0.49)

Market-to-Book Ratio × NEG -0.0114*** -0.00588** -0.0188***

(-4.64) (-2.28) (-4.17)

Market-to-Book Ratio × RET × NEG -0.0587*** -0.0335*** -0.0916***

(-5.76) (-3.09) (-4.89)

Book Leverage × RET 0.0180 0.0398 -0.0123

(0.71) (1.53) (-0.32)

Book Leverage × NEG 0.293*** 0.292*** 0.261**

(4.23) (3.26) (2.34)

Book Leverage × RET × NEG 1.392*** 1.442*** 1.293***

(5.15) (3.79) (3.01)

Number of Observations 22407 11205 11202

Adjusted R-Squared 0.237 0.243 0.243

Standard Errors Clustered By: Firm Firm Firm

Firm Fixed Effects: Yes Yes Yes

Year Fixed Effects: Yes Yes Yes

Table 10: The Effect of FINSA on Financial Reporting Conservatism – Test of the Basu (1997) Model

This table reports results from estimating the Basu (1997) earnings-return model. Basu (1997) model coefficients are interacted with

FINSA DiD variables to examine whether FINSA affected financial reporting conservatism. Main effects subsumed by firm and year

fixed effects are Post-FINSA, a variable equal to one in the years after 2008 and zero otherwise, and Treatment Industry, a variable equal

to one when the firm is in a FINSA-affected industry and zero otherwise. The dependent variable in all columns is net income measured

as income before extraordinary items divided by total market value. RET is the annual return compounded monthly for the twelve

months leading up to the fiscal year end. NEG is an indicator variable equal to one when RET is negative. Column (1) examines the full

sample. Column 2 (3) examines low (high) takeover probability firms. Other variables are defined in Appendix A. Firm and year fixed

effects are included, and standard errors are clustered by firm. T-statistics are presented underneath the coefficient estimates. ***, **, and *

denote two-tailed significance levels at 1%, 5%, and 10%, respectively.

55

(1) (2) (3) (4) (5) (6) (7) (8)

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Treatment Industry × Post-FINSA -0.000649 0.00500* 0.00235 -0.00209 -0.00248 0.00111 -0.00307 -0.00997***

(-0.18) (1.81) (0.79) (-0.99) (-0.74) (0.48) (-0.74) (-3.11)

Cash Flowst -2 -0.00273 -0.0144 -0.00104 0.0107 0.00657 0.000116 0.0133 0.0127

(-0.33) (-1.35) (-0.16) (1.00) (0.85) (0.01) (1.32) (0.82)

Cash Flowst +2 0.0153** 0.0339*** -0.00250 -0.0145 0.00535 0.00231 -0.0231** -0.0390***

(1.98) (2.75) (-0.40) (-1.27) (0.79) (0.20) (-2.38) (-2.78)

Loss Indicator -0.0684*** -0.0637*** 0.00869*** 0.0144*** -0.0371*** -0.0310*** 0.0650*** 0.0561***

(-25.24) (-22.16) (3.69) (5.76) (-14.15) (-10.09) (20.08) (18.14)

Operating Cycle 0.000234*** 0.000109* 0.000234*** 0.0000351 0.000208*** 0.0000536 0.0000941 0.0000586

(4.90) (1.76) (6.60) (0.62) (5.04) (1.01) (1.50) (0.66)

Stock Volatility 0.00110 0.00217* 0.00111 -0.000183 0.00174** 0.00107 -0.000574 -0.00377**

(1.17) (1.82) (1.43) (-0.18) (2.24) (1.18) (-0.50) (-2.21)

Standard Deviation of Earnings -0.0440*** -0.104*** 0.151*** 0.167*** 0.102*** 0.0678*** 0.169*** 0.226***

(-2.95) (-5.45) (14.61) (12.52) (8.07) (3.30) (9.58) (11.00)

Standard Deviation of Cash Flows 0.0187 0.0830*** -0.0446*** -0.0643*** -0.0348** 0.0169 -0.0306 -0.0632**

(1.15) (3.17) (-3.40) (-3.18) (-2.26) (0.70) (-1.43) (-2.37)

Industry Adjusted Cash Flows (INDADJCF) -0.423* -0.915* -0.00420 0.288 -0.155 -0.857* 0.308 0.290

(-1.66) (-1.87) (-0.02) (0.71) (-0.54) (-1.87) (1.41) (0.85)

Below-Industry Cash Flow Indicator (NEG-INDADJCF) 0.0213*** 0.0146*** 0.0306*** 0.0151*** 0.0247*** 0.0110*** -0.00409 -0.00932***

(11.05) (8.62) (16.44) (9.39) (11.93) (6.27) (-1.16) (-3.34)

INDADJCF × NEG-INDADJCF 0.285*** 0.243*** -0.458*** -0.407*** -0.133*** 0.0336 -0.505*** -0.459***

(11.40) (5.51) (-20.74) (-9.95) (-2.96) (0.70) (-16.95) (-9.03)




Table 7 Firm Controls: Yes Yes Yes Yes Yes Yes Yes Yes



Table 11: Decomposed Discretionary Accruals with Added Control Variables, Partitioned on Takeover Probability

This table replicates Table 7 after adding additional control variables. Variables are defined in Appendix A. T-statistics are presented underneath the coefficient estimates. ***, **, and * denote two-tailed significance levels at 1%, 5%, and 10%, respectively.

Performance-Adjusted (Kothari et al, 2005) Ball

and Shivakumar (2006) Modified Jones (1991)



(Kothari et al, 2005) Ball and Shivakumar (2006)



2005) Ball and Shivakumar (2006) Modified

Jones (1991) Discretionary Accruals




56

(1) (2) (3) (4) (5) (6) (7) (8)

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Treatment Industry × Post-FINSA 0.000163 0.00871*** -0.0000363 -0.00554*** -0.00345 0.00113 -0.00516 -0.0145***

(0.04) (2.94) (-0.01) (-2.59) (-0.96) (0.45) (-1.09) (-4.16)

Cash Flowst -1 0.0310*** 0.0136 -0.0226*** -0.0106 0.0103 -0.00233 -0.0283** -0.0125

(2.89) (0.82) (-2.62) (-0.81) (1.02) (-0.12) (-2.02) (-0.78)

Cash Flowst -0.858*** -0.934*** -0.0190 0.00649 -0.596*** -0.677*** 0.432*** 0.520***

(-47.91) (-39.71) (-1.41) (0.34) (-23.12) (-18.11) (18.31) (16.11)

Cash Flowst +1 0.00547 0.0446** -0.00769 0.00934 0.000308 0.0276** -0.0322** -0.0482***

(0.51) (2.49) (-0.95) (0.82) (0.03) (2.09) (-2.36) (-2.67)

Size -0.000813 -0.00591*** -0.0143*** -0.00948*** -0.0152*** -0.0136*** -0.00728*** -0.00446*

(-0.40) (-3.02) (-8.01) (-6.48) (-8.37) (-8.14) (-2.72) (-1.95)

Book Leverage -0.0654*** -0.0521*** 0.0203*** 0.0107 -0.0298*** -0.0310*** 0.0449*** 0.0333***

(-6.32) (-6.58) (2.69) (1.50) (-3.32) (-4.52) (3.87) (3.13)

Sales Growth -0.0183*** -0.0385*** 0.0295*** 0.0372*** 0.00974** 0.00405 0.0332*** 0.0506***

(-2.76) (-4.60) (6.35) (5.82) (2.02) (0.61) (3.81) (5.22)

Std(Sales) -0.00498 -0.0313*** 0.0736*** 0.0626*** 0.0527*** 0.0278*** 0.0650*** 0.0685***

(-0.61) (-4.16) (11.95) (8.96) (7.46) (4.30) (6.63) (7.36)

NOAt -1 0.00573 0.00523 -0.00344 -0.00749** 0.000901 -0.000675 -0.00788* -0.00614

(1.60) (1.50) (-1.34) (-2.58) (0.27) (-0.25) (-1.74) (-1.20)

Market Return 0.000692 0.00342** 0.00244*** 0.000504 0.00313*** 0.00164 0.00115 -0.00108

(0.66) (2.39) (2.77) (0.56) (3.67) (1.45) (0.87) (-0.69)


(-1.32) (-1.26) (2.19) (1.72) (-0.32) (0.67) (2.73) (1.32)

Property Ratio 0.0537*** 0.0217** -0.0426*** -0.0215*** -0.0147 -0.00348 -0.0688*** -0.0346***

(3.84) (2.02) (-4.32) (-2.60) (-1.24) (-0.40) (-4.71) (-2.78)

Liqudiity Ratio 0.0566*** 0.0238** -0.0110* 0.00372 0.00567 0.0102 -0.0445*** -0.0112

(6.64) (2.25) (-1.70) (0.50) (0.73) (1.24) (-4.73) (-1.01)


(-4.44) (-4.92) (4.46) (3.27) (1.54) (1.25) (6.00) (5.19)

Return on Assets 0.562*** 0.576*** -0.00624 -0.0665*** 0.407*** 0.384*** -0.298*** -0.349***

(32.89) (22.02) (-0.55) (-3.73) (20.92) (14.37) (-15.56) (-13.68)

HHI -16.39 -0.00765 13.53 0.0527** 0.558 0.0486*** 16.16 0.444**

(-1.28) (-0.26) (1.25) (2.23) (0.08) (3.02) (1.17) (1.97)

Foreign Sales 0.278*** 0.250*** -0.0989*** -0.143*** 0.0506* 0.0728*** -0.311*** -0.272***

(7.23) (7.52) (-3.65) (-6.16) (1.79) (2.86) (-7.46) (-7.84)


(-1.55) (-2.12) (3.19) (2.72) (1.73) (0.77) (1.43) (2.23)


(-2.51) (1.45) (0.50) (-0.96) (-0.16) (2.35) (0.73) (-0.58)






Table 12: Decomposed Discretionary Accruals, Partitioned on Takeover Probability, Excluding Lobbying Firms

This table replicates Table 7 after adding excludng firms that lobbied for or against FINSA. Variables are defined in Appendix A. T-statistics are presented underneath the coefficient estimates. ***, **, and * denote two-tailed significance levels at 1%,

5%, and 10%, respectively.













57

(1) (2) (3) (4) (5) (6) (7) (8)

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Placebo Industry × Post-FINSA 0.00117 0.00753** -0.0000594 -0.00587** -0.00256 0.00105 -0.00790 -0.0113***

(0.28) (2.18) (-0.02) (-2.40) (-0.58) (0.34) (-1.51) (-2.99)

Cash Flowst -1 0.0237* 0.0164 -0.0199** 0.00133 0.0111 0.0114 -0.0244 -0.0129

(1.93) (0.82) (-2.13) (0.10) (0.90) (0.49) (-1.60) (-0.75)

Cash Flowst -0.872*** -0.967*** -0.0309** 0.00416 -0.604*** -0.691*** 0.463*** 0.585***

(-44.46) (-36.71) (-2.04) (0.18) (-21.03) (-15.56) (16.89) (16.10)

Cash Flowst +1 0.00312 0.0529** -0.00218 0.00302 0.00141 0.0348** -0.0180 -0.0494**

(0.27) (2.56) (-0.24) (0.23) (0.14) (2.25) (-1.20) (-2.56)

Size -0.000571 -0.00557*** -0.0130*** -0.0108*** -0.0141*** -0.0148*** -0.00726** -0.00439*

(-0.26) (-2.62) (-6.74) (-7.07) (-7.21) (-7.25) (-2.44) (-1.86)

Book Leverage -0.0591*** -0.0512*** 0.0175** 0.00852 -0.0284*** -0.0324*** 0.0377*** 0.0385***

(-5.22) (-5.67) (2.17) (1.08) (-2.82) (-4.34) (3.00) (3.15)

Sales Growth -0.0203*** -0.0410*** 0.0310*** 0.0444*** 0.0108* 0.00900 0.0349*** 0.0540***

(-2.69) (-4.49) (6.04) (6.55) (1.87) (1.16) (3.78) (5.68)

Std(Sales) -0.000316 -0.0235*** 0.0706*** 0.0540*** 0.0525*** 0.0215*** 0.0577*** 0.0590***

(-0.04) (-2.97) (10.17) (7.17) (6.46) (3.07) (5.65) (6.11)

NOAt -1 0.00492 0.00583 -0.00391 -0.00905*** 0.00259 -0.000155 -0.00788 -0.0114**

(1.16) (1.54) (-1.36) (-3.22) (0.65) (-0.06) (-1.54) (-2.11)

Market Return -0.000921 0.00227 0.00250*** 0.00153 0.00215** 0.00336** 0.00169 0.000541

(-0.81) (1.26) (2.63) (1.31) (2.37) (2.14) (1.18) (0.33)

Market-to-Book Ratio -0.000322 -0.000485 0.000758** 0.000308 0.000167 -0.0000550 0.00130*** 0.000460

(-0.68) (-1.53) (2.19) (1.14) (0.36) (-0.13) (2.86) (1.27)

Property Ratio 0.0470*** 0.0355*** -0.0394*** -0.0318*** -0.0229* -0.00719 -0.0684*** -0.0509***

(3.13) (3.07) (-3.87) (-3.63) (-1.72) (-0.76) (-4.89) (-3.90)

Liqudiity Ratio 0.0533*** 0.0241** -0.00695 -0.00272 0.00723 0.00408 -0.0452*** -0.0154

(5.51) (2.18) (-1.02) (-0.31) (0.77) (0.44) (-4.54) (-1.23)

Change in Employees -0.0150*** -0.0236*** 0.0151*** 0.0102*** 0.00899** 0.00186 0.0326*** 0.0292***

(-3.34) (-5.49) (4.43) (2.92) (2.29) (0.43) (5.79) (5.94)

Return on Assets 0.588*** 0.604*** -0.0115 -0.0730*** 0.405*** 0.378*** -0.339*** -0.400***

(30.71) (20.12) (-0.88) (-3.48) (18.26) (12.31) (-15.64) (-13.72)

HHI -4.320 -0.0304 0.524 0.0409* 9.140 0.0228 1.206 0.413

(-1.34) (-0.70) (0.19) (1.66) (0.90) (1.00) (0.34) (1.51)

Foreign Sales 0.227*** 0.175*** -0.0597** -0.112*** 0.0613* 0.0452 -0.242*** -0.213***

(5.81) (5.05) (-2.00) (-4.56) (1.81) (1.55) (-5.56) (-5.89)

Number of Analysts Following -0.000990** -0.000462** 0.00103*** 0.000615*** 0.000262 0.000260 0.000755 0.000664**

(-2.56) (-2.19) (2.85) (3.54) (0.68) (1.29) (1.57) (2.56)

Institutional Ownership -0.0173*** 0.00142 0.00432 -0.00382 -0.00152 0.00537 0.0112 -0.00277

(-3.34) (0.35) (0.91) (-0.98) (-0.28) (1.55) (1.63) (-0.49)






Table 13: Earnings Management for CFIUS Firms After FINSA, Excluding Financial Recession Years

This table replicates Table 7 after excluding financial recession years (2007, 2008 and 2009). Variables are defined in Appendix A. T-statistics are presented underneath the coefficient estimates. ***, **, and * denote two-tailed significance levels at 1%, 5%,

and 10%, respectively.













58

(1) (2) (3) (4) (5) (6) (7) (8)

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Low Takeover

Probability

High Takeover

Probability

Placebo Industry × Post-FINSA 0.000535 -0.0000629 -0.00197 0.000730 -0.00164 0.0000997 -0.00135 -0.000897

(0.23) (-0.04) (-0.98) (0.50) (-0.73) (0.06) (-0.48) (-0.40)

Cash Flowst -1 0.0310*** 0.0132 -0.0226*** -0.0102 0.0103 -0.00233 -0.0283** -0.0129

(2.89) (0.79) (-2.62) (-0.78) (1.01) (-0.12) (-2.02) (-0.80)

Cash Flowst -0.858*** -0.935*** -0.0191 0.00669 -0.595*** -0.676*** 0.432*** 0.522***

(-47.94) (-39.88) (-1.42) (0.35) (-23.09) (-18.14) (18.29) (16.16)

Cash Flowst +1 0.00548 0.0446** -0.00773 0.00931 0.000278 0.0276** -0.0320** -0.0482***

(0.51) (2.49) (-0.96) (0.82) (0.03) (2.09) (-2.35) (-2.67)

Size -0.000812 -0.00577*** -0.0143*** -0.00958*** -0.0152*** -0.0135*** -0.00744*** -0.00458**

(-0.40) (-2.98) (-8.00) (-6.59) (-8.35) (-8.16) (-2.78) (-2.00)

Book Leverage -0.0654*** -0.0525*** 0.0203*** 0.0111 -0.0297*** -0.0305*** 0.0448*** 0.0340***

(-6.32) (-6.62) (2.69) (1.57) (-3.30) (-4.43) (3.86) (3.20)

Sales Growth -0.0183*** -0.0387*** 0.0295*** 0.0368*** 0.00984** 0.00409 0.0334*** 0.0503***

(-2.76) (-4.66) (6.37) (5.81) (2.05) (0.62) (3.84) (5.25)

Std(Sales) -0.00498 -0.0316*** 0.0736*** 0.0626*** 0.0528*** 0.0272*** 0.0652*** 0.0681***

(-0.61) (-4.22) (11.96) (9.02) (7.49) (4.24) (6.64) (7.31)

NOAt -1 0.00573 0.00517 -0.00345 -0.00747** 0.000880 -0.000800 -0.00791* -0.00601

(1.60) (1.49) (-1.34) (-2.58) (0.26) (-0.30) (-1.75) (-1.18)

Market Return 0.000691 0.00336** 0.00245*** 0.000558 0.00314*** 0.00162 0.00115 -0.00103

(0.65) (2.35) (2.78) (0.62) (3.69) (1.44) (0.87) (-0.66)


(-1.32) (-1.33) (2.19) (1.73) (-0.33) (0.64) (2.74) (1.41)

Property Ratio 0.0536*** 0.0232** -0.0426*** -0.0216*** -0.0144 -0.00262 -0.0682*** -0.0358***

(3.83) (2.19) (-4.31) (-2.65) (-1.21) (-0.31) (-4.68) (-2.89)

Liqudiity Ratio 0.0566*** 0.0233** -0.0110* 0.00389 0.00608 0.0103 -0.0442*** -0.0104

(6.65) (2.19) (-1.70) (0.52) (0.78) (1.25) (-4.69) (-0.94)


(-4.44) (-4.91) (4.46) (3.25) (1.54) (1.20) (5.99) (5.17)

Return on Assets 0.562*** 0.575*** -0.00623 -0.0663*** 0.407*** 0.382*** -0.298*** -0.349***

(32.91) (22.00) (-0.55) (-3.73) (20.89) (14.38) (-15.55) (-13.65)

HHI -16.40 -0.0240 13.56 0.0441** 1.170 0.0321 16.25 0.440**

(-1.28) (-0.57) (1.25) (2.10) (0.16) (1.47) (1.18) (2.12)

Foreign Sales 0.278*** 0.249*** -0.0988*** -0.143*** 0.0495* 0.0696*** -0.310*** -0.273***

(7.23) (7.49) (-3.64) (-6.19) (1.74) (2.75) (-7.46) (-7.83)


(-1.55) (-2.13) (3.19) (2.81) (1.74) (0.86) (1.43) (2.33)


(-2.51) (1.50) (0.50) (-0.99) (-0.14) (2.38) (0.75) (-0.58)






Table 14: Earnings Management for CFIUS Firms After FINSA, Randomized Treatment Placebo Test

This table reports results from regressions of accruals on firm-level characteristics including an interactive indicator variable capturing the years following FINSA and Placebo Industry firms. Main effects subsumed by firm and year fixed effects are Post-

FINSA, a variable equal to one in the years after 2008 and zero otherwise, and Placebo Industry, a variable equal to one when the firm is in a randomly selected placebo industry and zero otherwise. The dependent variable in column 1 and 2 (3 and 4) is

total accruals (performance-adjusted (Kothari et al., 2005) modified Jones (Dechow, Sweeney and Sloan (1995) discretionary accruals). Samples examined in odd (even) columns are low (high) takeover probability firms. Other variables are defined in

Appendix A. Firm and year fixed effects are included, and standard errors are clustered by firm. T-statistics are presented underneath the coefficient estimates. ***, **, and * denote two-tailed significance levels at 1%, 5%, and 10%, respectively.













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